Thyroid nodule diagnosis method based on deep learning network
A deep learning network, thyroid nodule technology, applied in the field of image processing and artificial intelligence-assisted diagnosis of diseases, can solve problems such as difficulty in obtaining better results, high noise in ultrasound images, and lack of doctor resources, reducing time and work. Intensity and stress, the effect of reducing economic and psychological burden
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Embodiment 1
[0051] Such as figure 1 As shown, an embodiment of a method for diagnosing a thyroid nodule based on a deep learning network of the present invention comprises the following steps:
[0052] A. Establishment of Thyroid Ultrasound Image Database
[0053] By collecting ultrasound images and pathology reports of thyroid patients in the pathology department and radiology department, the thyroid nodules were outlined and labeled using the labelme tool of Anaconda2 and the data was saved in josn format for subsequent use.
[0054] B Deeplab v3+ semantic segmentation based on Xception-JFT
[0055] a. Input the preprocessed ultrasound image into the segmentation model of Xception-JFT with Xception as the backbone network;
[0056] b. The network first performs feature extraction and downsampling through a 3×3 convolutional layer, and at the same time, the feature matrix is input into the Decoder part after four times downsampling;
[0057] c. After the feature extraction is complete...
Embodiment 2
[0064] In other specific implementations of the present invention, the rest are the same as the above-mentioned implementations, the difference is that, as figure 1 As shown, the preprocessing process of the image marked in step A can enhance the stability of the network operation; the preprocessing includes the following steps
[0065] a. Process the marked image with python and opencv to generate two labeled images in BMP format, and set different pixel values for thyroid nodules, parenchyma and other parts, so as to display different brightness to distinguish each part of the thyroid;
[0066] b. Generate a grayscale image from the marked image. The value of the gray-white channel is the matrix obtained by multiplying the grayscale image with the corresponding elements of the feature matrix of the mask of the thyroid parenchyma containing nodules. The gray channel only contains nodules. For multiple In the case of nodules, each nodule is distributed on the red channel of ...
Embodiment 3
[0070] In other specific implementations of the present invention, the rest are the same as the above-mentioned implementations, the difference is that, as Figure 4 As shown, Efficient Net B7 is selected for classification deep learning network:
[0071] a. Through the Efficient Net B7 model, the preprocessed input thyroid ultrasound image is subjected to feature extraction and reduction through a 3×3 convolution kernel,
[0072] b. Then use seven sets of moving reverse bottleneck convolutions to perform feature extraction at different scales,
[0073] c. Finally, use the fully connected layer to integrate features and use softmax to classify,
[0074] d. Output a two-dimensional vector containing benign probability and malignant probability of thyroid nodules to complete the judgment of benign and malignant thyroid nodules.
[0075] Efficient Net is a new model scaling method with extremely high parameter efficiency and speed. This method uses a simple but efficient compou...
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