Serum data classification method and system based on Raman spectrum

Abstract

The invention discloses a serum data classification method and system based on Raman spectrum, and relates to the technical field of medical spectrum analysis. According to the method, TransU-Net and a frequency domain attention mechanism are adopted, and time domain and frequency domain characteristics are combined, so that the fluorescence noise separation capability is remarkably improved; double decoders are constructed, key Raman peak information is reserved, and the classification accuracy is effectively improved; according to the method, a metabolite-Raman peak relational graph is constructed, related components of a target tumor are accurately recognized through a multi-stage classification model, manual intervention is reduced, the problems of serious fluorescence interference, insufficient feature extraction, low classification precision and the like in traditional Raman spectrum analysis are solved, and efficient and reliable technical support is provided for early diagnosis and precise medical treatment of tumors.

Description

Technical Field

[0001] The present invention relates to the technical field of medical spectral analysis, and particularly relates to a method and system for classifying serum data based on Raman spectroscopy. Background Art

[0002] Raman spectroscopy technology has been widely used in medical spectral analysis due to its non-destructive and rapid detection advantages. However, in practical applications, it faces four major problems: fluorescence interference, limitations in feature extraction, singularity of classification models, and lack of metabolite correlation analysis, which limit its application effect in early tumor diagnosis. Traditional classification methods have significant deficiencies in aspects such as fluorescence removal effect, feature expression ability, model generalization ability, and metabolic network relationship modeling. They cannot effectively separate fluorescence noise from Raman signals, resulting in inaccurate feature extraction, low classification accuracy, poor robustness, and inability to provide biological explanations, making it difficult to meet the analysis requirements of complex clinical data and the requirements of medical staff for rapid and effective detection of patients' serum. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for classifying serum data based on Raman spectroscopy to improve the above technical problems.

[0004] To achieve the above invention purpose, the embodiments of the present invention provide the following technical solutions:

[0005] A method for classifying serum data based on Raman spectroscopy is provided, which includes:

[0006] Obtain the serum sample of the patient and perform sample pretreatment and Raman spectroscopy measurement to obtain the corresponding original Raman spectral data;

[0007] Perform fluorescence removal processing and data enhancement on the original Raman spectral data to obtain Raman spectral data;

[0008] Extract features from the Raman spectral data to obtain Raman data;

[0009] Input the Raman data and the original Raman spectral data into a tumor recognition and classification model to obtain a tumor classification result; the tumor classification result includes tumor and non-tumor.

[0010] Further, the fluorescence removal process uses a generative adversarial network; the generative adversarial network includes a generator and a discriminator; the generator uses a TransU-Net network; the TransU-Net network includes an encoder and a decoder connected in series; the encoder includes a first convolutional block, a second convolutional block, a third convolutional block, a Transformer module, and a fourth convolutional block connected in series in sequence; the structures of the first to third convolutional blocks are exactly the same; the fourth convolutional block includes two convolutional layers; the decoder includes a Raman decoding module and a fluorescence noise decoding module in parallel; the Raman decoding module includes a first deconvolutional block, a second deconvolutional block, a third deconvolutional block, and a fourth deconvolutional block connected in series in sequence and with exactly the same structure; the fluorescence noise decoding module includes a DWConv layer, a fifth convolutional block, and a Conv5 layer connected in series in sequence; the fifth convolutional block includes two convolutional layers.

[0011] The first convolutional block includes a Conv1 layer, a Conv2 layer, a Maxpool layer, and an FFT layer connected in series in sequence; the first deconvolutional block includes a Conv3 layer, a Conv4 layer, an Upsampling layer, and an IFFT layer connected in series in sequence.

[0012] The Transformer module includes N Transformer layers connected in series; each Transformer layer includes a Layer Norm1 layer, an MSA layer, a Layer Norm2 layer, and an MLP layer connected in series in sequence.

[0013] Further, the training process of the generative adversarial network includes:

[0014] Obtain the original Raman spectrum training data and its labels.

[0015] Input the original Raman spectrum training data into the encoder, and process it through the first to third convolutional blocks in sequence, and respectively output the first spectral encoding data to the third spectral encoding data at the corresponding levels.

[0016] Input the third spectral encoding data into the Transformer module, and output the spectral encoding key data.

[0017] Input the spectral encoding key data into the fourth convolutional block, and output the fourth spectral encoding data.

[0018] Input the first to fourth spectral encoding data into the Raman decoding module, and output the Raman spectrum training decoded data, that is, the generated training samples of the generator.

[0019] Input the first to fourth spectral encoding data into the fluorescence noise decoding module to obtain the fluorescence noise spectral data.

[0020] Input the generated training samples and their labels into the discriminator to obtain the corresponding discriminative training results;

[0021] Based on the discriminative training results, the generated training samples, and the fluorescence noise spectral data, calculate the loss function; the loss function includes a reconstruction loss function, an adversarial loss function, and a fluorescence supervision loss function;

[0022] Based on the loss function, adjust the weight parameters of the adversarial generation network.

[0023] Furthermore, the processing process of the first convolutional block includes:

[0024] Input the original Raman spectral training data into the Conv1 layer and output the initial convolutional spectral training data;

[0025] Input the initial convolutional spectral training data into the Conv2 layer and output the convolutional spectral training data;

[0026] Input the convolutional spectral training data into the Maxpool layer and output the spectral training pooling data;

[0027] Input the spectral training pooling data into the FFT layer and output the spectral training frequency domain data;

[0028] Concatenate the spectral training pooling data and the spectral training frequency domain data to obtain the first spectral encoding data.

[0029] Furthermore, the processing process of the first deconvolutional block of the Raman decoding module is:

[0030] Concatenate the third spectral encoding data and the fourth spectral encoding data to obtain the spectral encoding concatenated data;

[0031] Input the spectral encoding concatenated data into the Conv3 layer and output the first convolutional spectral decoding data;

[0032] Input the first convolutional spectral decoding data into the Conv4 layer and output the second convolutional spectral decoding data;

[0033] Input the second convolutional spectral decoding data into the Upsampling layer and output the spectral sampling decoding data;

[0034] Input the spectral sampling decoding data into the IFFT layer and output the spectral time domain decoding data;

[0035] Concatenate the spectral time domain decoding data and the spectral sampling decoding data and output the first spectral decoding data.

[0036] Furthermore, the processing process of the first Transformer layer in the Transformer module includes:

[0037] Input the third spectral encoding data into the Layer Norm1 layer, and the normalized third spectral encoding data is output;

[0038] Input the normalized third spectral encoding data into the MSA layer, and the spectral encoding tensor data is output;

[0039] Concatenate the spectral encoding tensor data and the third spectral encoding data to obtain the spectral encoding tensor concatenated data;

[0040] Input the spectral encoding tensor concatenated data into the Layer Norm2 layer, and the normalized spectral encoding tensor concatenated data is output;

[0041] Input the normalized spectral encoding tensor concatenated data into the MLP layer, and the first spectral encoding initial key data is output;

[0042] Concatenate the spectral encoding tensor concatenated data and the first spectral encoding initial key data to obtain the first spectral encoding key data.

[0043] Further, the tumor recognition and classification model includes an initial classification module and a final classification module; the initial classification module includes a parallel XGBoost model and a 1D-ResNet model; the final classification module includes a three-layer graph convolutional network, a self-attention layer, and a classification layer connected in series in sequence; the classification layer includes a pooling layer and a fully connected classification layer connected in series.

[0044] Further, the inputting the Raman data and the original Raman spectral data into the tumor recognition and classification model to obtain the tumor classification result includes:

[0045] Input the Raman data into the XGBoost model, and the first initial classification recognition result is output;

[0046] Input the Raman data and the original Raman spectral data into the 1D-ResNet model, and the second initial classification recognition result is output;

[0047] Based on the first initial classification recognition result and the second initial classification recognition result, calculate the initial tumor classification result through a weighted voting mechanism;

[0048] Based on the initial tumor classification result, obtain the corresponding metabolite and construct the corresponding relationship graph;

[0049] Input the Raman data corresponding to the initial tumor classification result and the relationship graph into the final classification module, and the tumor classification result is output.

[0050] Further, the obtaining the corresponding metabolite based on the initial tumor classification result and constructing the corresponding relationship graph includes:

[0051] Based on the initial tumor classification result, obtain the corresponding metabolite;

[0052] Perform peak matching on the metabolite and the initial tumor classification result to obtain the corresponding peak matching result;

[0053] Based on the peak matching result, use the metabolite interaction network to screen for relevant pathways;

[0054] Take the Raman peaks corresponding to the metabolite and the initial tumor classification result as nodes, and take the relevant pathways and the mapping relationship between the Raman peaks corresponding to the initial tumor classification result and the metabolite as edges to construct metabolite nodes, Raman peak nodes, metabolite-Raman peak edges, and metabolite-metabolite edges, and obtain the corresponding relationship graph.

[0055] A serum data classification system based on Raman spectroscopy is provided, which includes:

[0056] A Raman spectroscopy acquisition and processing module, which is used to obtain the serum sample of the patient and perform sample pretreatment and Raman spectroscopy measurement to obtain the corresponding original Raman spectroscopy data;

[0057] A Raman spectroscopy enhancement module, which is used to perform fluorescence removal processing and data enhancement on the original Raman spectroscopy data to obtain Raman spectroscopy data;

[0058] A Raman feature extraction module, which is used to extract features from the Raman spectroscopy data to obtain Raman data;

[0059] A tumor classification module, which is used to input the Raman data and the original Raman spectroscopy data into a tumor recognition and classification model to obtain a tumor classification result.

[0060] The beneficial effects of the present invention are:

[0061] The present invention adopts TransU-Net and a frequency-domain attention mechanism, combines time-domain and frequency-domain features, and significantly improves the fluorescence noise separation ability; constructs a dual decoder to retain key Raman peak information and effectively improves the classification accuracy; constructs a metabolite-Raman peak relationship graph, and through a multi-level classification model, accurately identifies target tumor-related components, reduces manual intervention, and solves problems such as serious fluorescence interference, insufficient feature extraction, and low classification accuracy in traditional Raman spectroscopy analysis, providing efficient and reliable technical support for precision medicine. Description of the Drawings

[0062] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present invention, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0063] Figure 1 It is the flowchart of the method in the embodiments of the present invention;

[0064] Figure 2 It is the structural diagram of the TransU-Net network in the embodiments of the present invention;

[0065] Figure 3 It is the structural diagram of the first convolutional block and the first transposed convolutional block in the embodiments of the present invention;

[0066] Figure 4 It is the structural diagram of the Transformer module in the embodiments of the present invention;

[0067] Figure 5 It is the structural diagram of the tumor recognition and classification model in the embodiments of the present invention;

[0068] Figure 6 It is the Raman spectrogram corresponding to IHC0 and IHC3 in the embodiments of the present invention;

[0069] Figure 7 It is the system structural diagram in the embodiments of the present invention. Specific Embodiments

[0070] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Usually, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0071] Please refer to Figure 1 , a method for classifying serum data based on Raman spectroscopy provided in this embodiment includes:

[0072] S1. Obtain the serum sample of the patient and perform sample preprocessing and Raman spectroscopy measurement to obtain the corresponding original Raman spectroscopic data;

[0073] S2. Perform fluorescence removal processing and data enhancement on the original Raman spectral data to obtain Raman spectral data;

[0074] In Raman spectroscopy analysis, fluorescence background interference is a common problem. Since Raman scattering signals are usually very weak, while the fluorescence background signals may be several orders of magnitude stronger than Raman scattering signals, this will cause the Raman scattering signals to be masked, making it difficult to identify characteristic peaks, thereby affecting the quality of the data and the accuracy of subsequent analysis. In addition, the original Raman spectral data itself has high-dimensional and complex non-linear relationships, which increases the difficulty of feature extraction and classification. Traditional feature extraction methods often cannot fully express these complex features. Especially in the case of limited sample numbers, the model is prone to overfitting problems. Therefore, the S2 includes:

[0075] S2-1. Use an adversarial generative network to perform fluorescence removal processing on the original Raman spectral data to obtain processed Raman spectral data;

[0076] The adversarial generative network includes a generator and a discriminator; as Figure 2 shown, the generator adopts a TransU-Net network; the TransU-Net network includes an encoder and a decoder connected in series; the encoder includes a first convolutional block, a second convolutional block, a third convolutional block, a Transformer module, and a fourth convolutional block connected in series in sequence; the structures of the first convolutional block to the third convolutional block are exactly the same; the fourth convolutional block includes two convolutional layers; the decoder includes a Raman decoding module and a fluorescence noise decoding module in parallel; the Raman decoding module includes a first transposed convolutional block, a second transposed convolutional block, a third transposed convolutional block, and a fourth transposed convolutional block connected in series in sequence and with exactly the same structure; the fluorescence noise decoding module includes a DWConv layer, a fifth convolutional block, and a Conv5 layer connected in series in sequence; the fifth convolutional block includes two convolutional layers;

[0077] as Figure 3 shown, the first convolutional block includes a Conv1 layer, a Conv2 layer, a Maxpool layer (maximum pooling layer), and an FFT layer (Fourier transform layer) connected in series in sequence; the first transposed convolutional block includes a Conv3 layer, a Conv4 layer, an Upsampling layer (upsampling layer), and an IFFT layer (inverse Fourier transform layer) connected in series in sequence;

[0078] as Figure 4 shown, the Transformer module includes N Transformer layers connected in series; each Transformer layer includes a Layer Norm1 layer, an MSA (multi-head attention mechanism) layer, a Layer Norm2 layer, and an MLP layer (multi-layer perceptron) connected in series in sequence. In this embodiment, N is 3.

[0079] The training process of the adversarial generative network includes:

[0080] S2-1-1. Obtain the original Raman spectrum training data and its labels; the labels are the original Raman spectrum training data after removing fluorescence noise.

[0081] S2-1-2. Input the original Raman spectrum training data into the encoder, and process it through the first convolutional block to the third convolutional block in sequence, and respectively output the first spectral encoding data to the third spectral encoding data corresponding to each level. Specifically, input the original Raman spectrum training data into the first convolutional block to obtain the first spectral encoding data; input the first spectral encoding data into the second convolutional block to obtain the second spectral encoding data; input the second spectral encoding data into the third convolutional block to obtain the third spectral encoding data.

[0082] Please refer to Figure 3 and the processing process of the first convolutional block is as follows:

[0083] S2-1-2-1. Input the original Raman spectrum training data into the Conv1 layer, and output the initial convolutional spectrum training data.

[0084] S2-1-2-2. Input the initial convolutional spectrum training data into the Conv2 layer, and output the convolutional spectrum training data.

[0085] S2-1-2-3. Input the convolutional spectrum training data into the Maxpool layer, and output the spectral training pooling data.

[0086] S2-1-2-4. Input the spectral training pooling data into the FFT layer, and output the spectral training frequency domain data.

[0087] S2-1-2-5. Concatenate the spectral training pooling data and the spectral training frequency domain data to obtain the first spectral encoding data.

[0088] The second convolutional block and the third convolutional block adopt the same method as S2-1-2-1 to S2-1-2-5, so they will not be elaborated here.

[0089] This method introduces a frequency domain attention mechanism (fast Fourier transform) in the first convolutional block to the third convolutional block, enhances the ability of the adversarial generative network to distinguish fluorescence (low frequency) and Raman peaks (high frequency), avoids the limitation of traditional convolution that only focuses on local time domain features, and concatenates the spectral training pooling data and the spectral training frequency domain data to retain multi-scale information.

[0090] S2-1-3. Input the third spectral encoding data into the Transformer module, and use N Transformer layers to extract and capture global dependencies and perform feature transformation, and output the spectral encoding key data.

[0091] Please refer to Figure 4 , the processing process of the first Transformer layer in the Transformer module includes:

[0092] S2-1-3-1. Input the third spectral encoding data into the Layer Norm1 layer, and output the normalized third spectral encoding data;

[0093] S2-1-3-2. Input the normalized third spectral encoding data into the MSA (Multi-Head Attention Mechanism) layer, capture the dependencies between different positions, splice the outputs of each attention head, and output the spectral encoding tensor data;

[0094] S2-1-3-3. Splice the spectral encoding tensor data and the third spectral encoding data to obtain the spectral encoding tensor splicing data;

[0095] S2-1-3-4. Input the spectral encoding tensor splicing data into the Layer Norm2 layer, and output the normalized spectral encoding tensor splicing data;

[0096] S2-1-3-5. Input the normalized spectral encoding tensor splicing data into the MLP (Multi-Layer Perceptron) layer for non-linear transformation to enhance the feature expression ability, and output the first spectral encoding initial key data;

[0097] S2-1-3-6. Splice the spectral encoding tensor splicing data and the first spectral encoding initial key data to obtain the first spectral encoding key data.

[0098] The processing processes of the other Transformer layers are the same as that of the first Transformer layer, and the output of the last Transformer layer is the spectral encoding key data.

[0099] S2-1-4. Input the spectral encoding key data into the fourth convolutional block, and output the fourth spectral encoding data;

[0100] S2-1-5. Input the first spectral encoding data to the fourth spectral encoding data into the Raman decoding module, and output the Raman spectral training decoding data, that is, the generated training samples of the generator (pure Raman signal false data without fluorescence noise);

[0101] The S2-1-5 includes:

[0102] S2-1-5-1. Splice the third spectral encoding data and the fourth spectral encoding data to obtain the spectral encoding splicing data;

[0103] S2-1-5-2. Input the spectral encoding splicing data into the first deconvolution block and output the first spectral decoding data;

[0104] Please refer to Figure 3 , where S2-1-5-2 includes:

[0105] Input the spectral encoding splicing data into the Conv3 layer and output the first convolutional spectral decoding data;

[0106] Input the first convolutional spectral decoding data into the Conv4 layer and output the second convolutional spectral decoding data;

[0107] Input the second convolutional spectral decoding data into the Upsampling layer and output the spectral sampling decoding data;

[0108] Input the spectral sampling decoding data into the IFFT layer and output the spectral time-domain decoding data;

[0109] Concatenate the spectral time-domain decoding data and the spectral sampling decoding data and output the first spectral decoding data.

[0110] The second to fourth deconvolution blocks adopt the same method as S2-1-5-2.

[0111] S2-1-5-3. Concatenate the first spectral decoding data and the second spectral encoding data to obtain the first spectral decoding splicing data;

[0112] S2-1-5-4. Input the first spectral decoding splicing data into the second deconvolution block and output the second spectral decoding data;

[0113] S2-1-5-5. Concatenate the second spectral decoding data and the first spectral encoding data to obtain the second spectral decoding splicing data;

[0114] S2-1-5-6. Input the second spectral decoding splicing data into the third deconvolution block and output the third spectral decoding data;

[0115] S2-1-5-7. Input the third spectral decoding data into the fourth deconvolution block and output the Raman spectrum training decoding data, that is, the processed Raman spectrum training data (generate training samples);

[0116] S2-1-6. Input the first to fourth spectral encoding data into the fluorescence noise decoding module to obtain the fluorescence noise spectral data;

[0117] Where S2-1-6 includes:

[0118] S2-1-6-1. Concatenate the third spectral encoded data and the fourth spectral encoded data to obtain the spectral encoded concatenated data;

[0119] S2-1-6-2. Input the spectral encoded concatenated data into the DWConv layer and output the first fluorescence noise decoded data;

[0120] S2-1-6-3. Concatenate the first fluorescence noise decoded data and the second spectral encoded data to obtain the first fluorescence noise concatenated decoded data;

[0121] S2-1-6-4. Input the first fluorescence noise concatenated decoded data into the fifth convolutional block and output the second fluorescence noise decoded data;

[0122] S2-1-6-5. Concatenate the second fluorescence noise decoded data and the first spectral encoded data to obtain the second fluorescence noise concatenated decoded data;

[0123] S2-1-6-6. Input the second fluorescence noise concatenated decoded data into the Conv5 layer and output the fluorescence noise spectral data.

[0124] S2-1-7. Input the generated training samples and their labels into the discriminator to obtain the corresponding discriminative training results;

[0125] S2-1-8. Calculate the loss function based on the discriminative training results, the generated training samples, and the fluorescence noise spectral data; the loss function includes a reconstruction loss function, an adversarial loss function, and a fluorescence supervision loss function;

[0126] Reconstruction loss function Adopt the L1 norm loss function, adversarial loss function Adopt the WGAN-GP loss function, fluorescence supervision loss function Adopt the MSE loss function, thus, the loss function The corresponding formula is:

[0127] ;

[0128] Among them, , , represent coefficients, , , The sum of is 1.

[0129] S2-1-9. Adjust the weight parameters of the adversarial generation network based on the loss function.

[0130] In actual use, only the generator performs fluorescence removal processing on the input original Raman spectral data to obtain the processed Raman spectral data.

[0131] This method constructs an adversarial generation network using TransU-Net and Fourier transform, designs a dual decoder, extracts the frequency-domain and time-domain features of Raman spectral data, which is beneficial to separating Raman scattering signals and fluorescence noise, highly preserves deep features, and can extract key characteristic peaks.

[0132] S2-2. Perform data enhancement on the processed Raman spectral data by operations such as baseline correction and smoothing filtering to obtain Raman spectral data.

[0133] S3. Extract features from the Raman spectral data to obtain Raman data; the feature extraction method used in S3 can adopt the wavelet transform method or use a CNN model for feature extraction, which can effectively extract the frequency-domain / spatial-domain features in the Raman spectrum and is beneficial for subsequent multi-level classification.

[0134] S4. Input the Raman data and the original Raman spectral data into the tumor recognition and classification model to obtain the tumor classification result; the classification result includes tumor and non-tumor, and can also include the status of a certain molecular subtype.

[0135] As Figure 5 shown, the tumor recognition and classification model includes an initial classification module and a final classification module; the initial classification module includes a parallel XGBoost model and a 1D-ResNet model; the final classification module includes a three-layer graph convolutional network (GCN network), a self-attention layer (Self-attention), and a classification layer connected in series in sequence; the classification layer includes a pooling layer and a fully connected classification layer connected in series.

[0136] S4 includes:

[0137] S4-1. Input the Raman data into the XGBoost model and output the first initial classification and recognition result.

[0138] S4-2. Input the Raman data and the original Raman spectral data into the 1D-ResNet model and output the second initial classification and recognition result.

[0139] S4-3. Based on the first initial classification and recognition result and the second initial classification and recognition result, calculate the initial tumor classification result through a weighted voting mechanism.

[0140] The training process of the initial classification module is:

[0141] S4-3-1. Obtain Raman training data and original Raman spectral training data.

[0142] S4-3-2. Input the Raman training data into the XGBoost model and output the first initial recognition and classification training result.

[0143] S4-3-3. Input the Raman training data and the original Raman spectrum data into the 1D-ResNet model, and output the second initial recognition and classification training result;

[0144] S4-3-4. Based on the first initial recognition and classification training result and the second initial recognition and classification training result, calculate the initial classification training result through a weighted voting mechanism;

[0145] S4-3-5. Based on the initial classification training result, adjust the weight parameters of the initial classification module.

[0146] S4-4. Based on the initial tumor classification result, obtain the corresponding metabolites and construct the corresponding relationship graph;

[0147] The S4-4 includes:

[0148] S4-4-1. Based on the initial tumor classification result, obtain the corresponding metabolites;

[0149] S4-4-2. Perform peak position matching on the metabolites and the initial tumor classification result to obtain the corresponding peak position matching result; Use the Bio-Rad KnowItAll database to map the Raman peaks corresponding to the initial tumor classification result to the metabolites, such as 1660 cm -1 →β-sheet protein;

[0150] S4-4-3. Based on the peak position matching result, use the metabolite interaction network (such as the STRING database) to screen relevant pathways; For example, when detecting breast cancer, the relevant pathways are the HER2 signaling pathway, glycolysis, etc.; The relevant pathways refer to the interaction relationships between each pair of metabolites;

[0151] S4-4-4. Take the metabolites and the Raman peaks corresponding to the initial tumor classification result as nodes, and take the relevant pathways and the mapping relationship between the Raman peaks corresponding to the initial tumor classification result and the metabolites as edges to construct metabolite nodes, Raman peak nodes, metabolite-Raman peak edges and metabolite-metabolite edges, and obtain the corresponding relationship graph.

[0152] S4-5. Input the Raman data and the relationship graph corresponding to the initial tumor classification result into the final classification module, and output the tumor classification result.

[0153] Thus, the training process of the graph convolutional network is:

[0154] S4-5-1. Obtain the Raman training data and the relationship training graph corresponding to the initial classification training result;

[0155] S4-5-2. Input the Raman training data and the relationship training graph corresponding to the initial classification training result into the first-layer graph convolutional network, and output the first-order adjacency training information;

[0156] S4-5-3. Input the first-order adjacency training information into the second-layer graph convolutional network, and output the second-order adjacency training information;

[0157] S4-5-4. Input the second-order adjacency training information into the third-layer graph convolutional network, and output the third-order adjacency training information;

[0158] S4-5-5. Input the third-order adjacency training information into the self-attention layer, and use the attention mechanism to extract key features to obtain the key adjacency training features;

[0159] S4-5-6. Input the key adjacency training features into the classification layer, and output the final classification training result;

[0160] S4-5-7. Based on the final classification training result, adjust the weight parameters of the target tumor recognition classification model.

[0161] This method realizes the rapid localization of breast cancer-related components from complex Raman spectra through the efficient rough screening of the first-level classification model and the accurate recognition of the second-level classification model.

[0162] Taking breast cancer as an example, select the serum sample data of a certain hospital for experiments, classify the Raman peaks corresponding to HER-2 in the serum samples of patients, and distinguish the Raman spectra corresponding to the two categories of IHC0 and IHC3.

[0163] The Raman data corresponding to IHC0 and IHC3 are as Figure 6 shown. The Raman peaks of the two groups basically coincide, but there are inter-group differences in the intensity of the Raman scattering signals. The Raman peaks of the two groups at wavenumbers of 824 cm -1 , 849 cm -1 and 869 cm -1 are related to the glucose component in the serum sample; the Raman peaks at wavenumbers of 1102 cm -1 , 1095 cm -1 and 1097 cm -1 are related to the lipid component in the serum sample; the Raman peaks at wavenumbers of 1451 cm -1 , 1461 cm -1 , 1463 cm -1 , 1465 cm -1 , 1468 cm -1 and 1479 cm -1 are related to the lipid component and the protein component in the serum sample; the Raman peaks at wavenumbers of 1308 cm -1 and 1305 cm-1 The Raman peaks at [location] are related to the carotenoid components in the serum sample; at wavenumbers of 1655 cm -1 、1661 cm -1 、1665 cm -1 、1672 cm -1 、1677 cm -1 and 1679 cm -1 the Raman peaks at [location] are related to the phospholipid components and fatty acid components in the serum sample. The differences in Raman spectra between the two categories of IHC0 and IHC3 reflect the changes in biochemical components, providing a basis for distinguishing different types of IHC classification.

[0164] Calculate the evaluation indexes of this method (MSA-FFT-U Net-XGBoost-ResNet-GCN), method 1 (MSA-U Net-XGBoost-ResNet-GCN) without introducing the multi-head attention mechanism, and method 2 (FFT-U Net-XGBoost-ResNet-GCN) without introducing the frequency-domain attention mechanism.

[0165] The evaluation indexes selected are accuracy, AUC score, and F1 score, and the corresponding values are shown in Table 1.

[0166] Table 1

[0167]

[0168] As can be seen from Table 1, the accuracy, AUC score, and F1 score of this method are 0.967, 0.970, and 0.969 respectively; the accuracy, AUC score, and F1 score of method 1 are 0.905, 0.908, and 0.893 respectively; the accuracy, AUC score, and F1 score of method 2 are 0.882, 0.895, and 0.888 respectively. Therefore, the accuracy, AUC score, and F1 score of this method are all higher than those of method 1 and method 2, further proving that introducing the attention mechanism in this method can effectively improve the feature processing ability and classification accuracy of the model.

[0169] As Figure 7 shown, a serum data classification system based on Raman spectroscopy includes:

[0170] A Raman spectroscopy acquisition and processing module, which is used to obtain the serum sample of the patient and perform sample preprocessing and Raman spectroscopy measurement to obtain the corresponding original Raman spectroscopy data;

[0171] A Raman spectroscopy enhancement module, which is used to perform fluorescence removal processing and data enhancement on the original Raman spectroscopy data to obtain Raman spectroscopy data;

[0172] A Raman feature extraction module, which is used to extract features from Raman spectroscopy data to obtain Raman data;

[0173] A tumor classification module, which is used to input the Raman data and the original Raman spectroscopy data into a tumor recognition and classification model to obtain a tumor classification result.

[0174] In summary, the present invention combines time-domain and frequency-domain features through an adversarial generative network (TransU-Net + frequency-domain attention mechanism), significantly improving the fluorescence noise separation ability. At the same time, a dual-decoder design (Raman signal decoding + fluorescence noise decoding) is adopted to retain key Raman peak information. In terms of classification, parallel XGBoost (feature importance analysis) and 1D-ResNet (deep feature extraction) improve the initial classification accuracy through weighted voting. At the same time, a "metabolite-Raman peak" relationship graph is constructed, combined with a graph convolutional network (GCN) and a self-attention mechanism, to accurately identify target tumor-related components. In addition, a secondary classification model (initial classification + target tumor recognition) realizes progressive analysis from rough screening to fine segmentation, and outputs probabilities and key metabolite-Raman peak pairs. The system adopts a modular design and step-by-step processing (data acquisition → fluorescence removal and enhancement → feature extraction → classification) to improve scalability and maintainability. At the same time, an adversarial generative network and a multi-level classification model are integrated to reduce manual intervention and are applicable to the rapid analysis of large-scale clinical data.

[0175] The above is only a specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, and all of them should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A serum data classification method based on Raman spectroscopy, characterized in that: include: Obtaining a serum sample from the patient and performing sample pretreatment and Raman spectrum measurement to obtain corresponding original Raman spectrum data; Perform defluorescence processing and data enhancement on the original Raman spectrum data to obtain Raman spectrum data; Extracting features from Raman spectrum data to obtain Raman data; The Raman data and the original Raman spectrum data are input into the tumor recognition and classification model to obtain a tumor classification result; the tumor classification result includes tumors and non-tumors.

2. The serum data classification method based on Raman spectroscopy according to claim 1, characterized in that: The defluorescence treatment adopts a generative adversarial network; the generative adversarial network includes a generator and a discriminator; the generator adopts a TransU-Net network; the TransU-Net network includes a serially connected encoder and decoder; the encoder includes a first convolution block, a second convolution block, a third convolution block, a Transformer module and a fourth convolution block; the structures of the first convolution block to the third convolution block are exactly the same; the fourth convolution block includes two convolution layers; the decoder includes a parallel Raman decoding module and a fluorescence noise decoding module; the Raman decoding module includes a first deconvolution block, a second deconvolution block, a third deconvolution block and a fourth deconvolution block that are serially connected and have exactly the same structure; the fluorescence noise decoding module includes a DWConv layer, a fifth convolution block and a Conv5 layer that are serially connected; the fifth convolution block includes two convolution layers; The first convolution block includes a Conv1 layer, a Conv2 layer, a Maxpool layer, and an FFT layer connected in series in sequence; the first deconvolution block includes a Conv3 layer, a Conv4 layer, an Upsampling layer, and an IFFT layer connected in series in sequence; The Transformer module includes N layers of Transformer layers connected in series; each Transformer layer includes a Layer Norm1 layer, an MSA layer, a Layer Norm2 layer, and an MLP layer connected in series in sequence.

3. The serum data classification method based on Raman spectroscopy according to claim 2, characterized in that: The training process of the adversarial generative network includes: Get the original Raman spectrum training data and its labels; The original Raman spectrum training data is input into the encoder, and processed by the first convolution block to the third convolution block in sequence, and the first spectrum encoding data to the third spectrum encoding data of the corresponding levels are output respectively; Input the third spectral encoding data into the Transformer module, and output the spectral encoding key data; Inputting the spectrum encoding key data into the fourth convolution block, and outputting the fourth spectrum encoding data; Input the first spectrum encoding data to the fourth spectrum encoding data into the Raman decoding module, and output Raman spectrum training decoded data, that is, the generated training samples of the generator; Inputting the first spectrum encoding data to the fourth spectrum encoding data into a fluorescence noise decoding module to obtain fluorescence noise spectrum data; Input the generated training samples and their labels into the discriminator to obtain the corresponding discriminant training results; Based on the discriminant training results, generated training samples and fluorescence noise spectrum data, a loss function is calculated; the loss function includes a reconstruction loss function, an adversarial loss function and a fluorescence supervision loss function; Based on the loss function, adjust the weight parameters of the adversarial generation network.

4. The serum data classification method based on Raman spectroscopy according to claim 3 is characterized in that: The processing process of the first convolution block includes: The original Raman spectrum training data is input into the Conv1 layer, and the initial convolution spectrum training data is output; Input the initial convolution spectrum training data into the Conv2 layer, and output the convolution spectrum training data; The convolution spectrum training data is input into the Maxpool layer, and the spectrum training pooling data is output; Input the spectral training pooling data into the FFT layer, and output the spectral training frequency domain data; The spectral training pooling data and the spectral training frequency domain data are concatenated to obtain first spectral encoding data.

5. The serum data classification method based on Raman spectroscopy according to claim 3, characterized in that: The processing process of the first deconvolution block of the Raman decoding module is: splicing the third spectral coded data and the fourth spectral coded data to obtain spectral coded spliced ​​data; The spectral encoding splicing data is input into the Conv3 layer, and the first convolutional spectral decoding data is output; The first convolution spectrum decoding data is input into the Conv4 layer, and the second convolution spectrum decoding data is output; The second convolution spectrum decoding data is input to the Upsampling layer, and the spectrum sampling decoding data is output; Input the spectrum sampling decoded data into the IFFT layer, and output the spectrum time domain decoded data; The spectral time-domain decoded data and the spectral sampling decoded data are spliced ​​and output to obtain first spectral decoded data.

6. The serum data classification method based on Raman spectroscopy according to claim 3, characterized in that: The processing process of the first Transformer layer in the Transformer module includes: Input the third spectrum encoding data to the Layer Norm1 layer, and output the normalized third spectrum encoding data; The normalized third spectrum encoding data is input into the MSA layer, and the spectrum encoding tensor data is output; splicing the spectral coding tensor data and the third spectral coding data to obtain spectral coding tensor splicing data; Input the spectral coding tensor splicing data into the Layer Norm2 layer, and output the normalized spectral coding tensor splicing data; The normalized spectral coding tensor concatenation data is input into the MLP layer, and the first spectral coding initial key data is output; The spectral coding tensor splicing data and the first spectral coding initial key data are spliced ​​to obtain the first spectral coding key data.

7. The serum data classification method based on Raman spectroscopy according to claim 1, characterized in that: The tumor recognition and classification model includes an initial classification module and a final classification module; the initial classification module includes a parallel XGBoost model and a 1D-ResNet model; the final classification module includes a three-layer graph convolutional network, a self-attention layer and a classification layer connected in series; the classification layer includes a series of pooling layers and a fully connected classification layer.

8. The serum data classification method based on Raman spectroscopy according to claim 7, characterized in that: The Raman data and the original Raman spectrum data are input into the tumor recognition and classification model to obtain the tumor classification result, including: The Raman data is input into the XGBoost model, and the first initial classification recognition result is obtained as output; The Raman data and the original Raman spectrum data are input into the 1D-ResNet model, and the second initial classification recognition result is obtained as output; Based on the first initial classification recognition result and the second initial classification recognition result, calculating an initial tumor classification result through a weighted voting mechanism; Based on the initial tumor classification results, the corresponding metabolites are obtained and the corresponding relationship graph is constructed; The Raman data and relationship diagram corresponding to the initial tumor classification result are input into the final classification module, and the tumor classification result is output.

9. The serum data classification method based on Raman spectroscopy according to claim 7, characterized in that: The method of obtaining corresponding metabolites based on the initial tumor classification results and constructing corresponding relationship diagrams includes: Based on the initial tumor classification results, the corresponding metabolites are obtained; Perform peak position matching on the metabolites and the initial tumor classification results to obtain corresponding peak position matching results; Based on the peak matching results, the metabolite interaction network was used to screen related pathways; The metabolites and Raman peaks corresponding to the initial tumor classification results are taken as nodes, and the mapping relationships between the related pathways and the Raman peaks corresponding to the initial tumor classification results and the metabolites are taken as edges. Metabolite nodes, Raman peak nodes, metabolite-Raman peak edges, and metabolite-metabolite edges are constructed to obtain the corresponding relationship graph.

10. A serum data classification system based on Raman spectroscopy, used to implement a serum data classification method based on Raman spectroscopy according to any one of claims 1 to 9, characterized in that: include: A Raman spectrum acquisition and processing module is used to obtain a serum sample of the patient and perform sample preprocessing and Raman spectrum measurement to obtain corresponding original Raman spectrum data; A Raman spectrum enhancement module is used to perform defluorescence processing and data enhancement on the original Raman spectrum data to obtain Raman spectrum data; A Raman feature extraction module is used to extract features from Raman spectrum data to obtain Raman data; The tumor classification module is used to input Raman data and original Raman spectrum data into the tumor recognition and classification model to obtain tumor classification results.

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