Multispectral Fluorescence Tissue Classification Using Deep Learning
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Solution Overview
Problem
Current methods for skin cancer screening, such as visual inspection and traditional fluorescence spectroscopy, are inefficient, subjective, and inaccurate due to reliance on extensive medical training and inability to handle high-dimensional fluorescence data effectively.
Innovation Solution
A non-invasive light-based sensor system using deep learning neural networks to analyze excitation-emission matrices from biological tissues, providing real-time classification and concentration analysis by integrating lasers with optical and electronic units for precise tissue characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fluorescence spectroscopy is used for skin cancer screening, then the method is non-invasive and provides chemical information, but it cannot effectively handle high-dimensional fluorescence data and relies on extensive medical training for accurate interpretation
Solution Approach 1:
The patent introduces machine learning models as an intermediary between the fluorescence spectroscopy data and the final diagnosis. The model processes the high-dimensional excitation-emission matrix data and outputs classification results, eliminating the need for medical professionals to manually interpret complex spectral data while maintaining high diagnostic accuracy
Solution Approach 2:
The patent replaces the manual interpretation process (requiring extensive medical training) with an automated computational system. The machine learning model automatically classifies tissue types based on fluorescence patterns, substituting human expert analysis with an algorithmic approach that is both accurate and easily operable
2Productivity
If visual inspection and traditional fluorescence spectroscopy are used for skin cancer screening, then the methods are non-invasive, but they are inefficient and subjective
Solution Approach 1:
The system performs self-service by automatically collecting fluorescence data, processing it through machine learning models, and generating diagnostic classifications without requiring subjective human interpretation. The algorithm objectively processes each case independently, eliminating variability between different medical professionals and improving both efficiency and reliability
Solution Approach 2:
The machine learning model is trained on labeled datasets containing known cancerous and non-cancerous tissue examples. This feedback mechanism allows the system to learn from previous cases and continuously improve its classification accuracy, providing objective and reliable diagnostic support
3Loss of information
If excitation-emission matrices are measured at multiple wavelengths, then comprehensive fluorescence data is obtained for accurate tissue characterization, but the data dimensionality increases making analysis more complex
Solution Approach 1:
The patent transforms the high-dimensional excitation-emission matrix data into a more manageable form by using machine learning models that can process and extract relevant features from the spectral data. The model learns optimal parameter representations during training, converting complex multi-wavelength data into meaningful classification outputs without losing diagnostic information
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves high accuracy in classifying skin tissues as normal, mole, or cancerous with a 99.8% recognition rate, offering a more reliable and efficient alternative to traditional screening methods.
Implementation Method 1
Fluorescence occurs when materials emit light in response to absorbing electromagnetic radiation. illuminating a biological tissue with stimulant light at a first wavelength to cause a first fluorescent emission of light by the biological tissue
Data Source
AI summary
Provided is a obtaining an excitation-emission matrix, wherein the excitation-emission matrix is measured with a spectrometer by: illuminating a biological tissue with stimulant light at a first wavelength to cause a first fluorescent emission of light by the biological tissue, measuring a first set of intensities of the first fluorescent emission of light at a plurality of different respective emission wavelengths, illuminating the biological tissue with stimulant light at a second wavelength to cause a second fluorescent emission of light by the biological tissue, and measuring a second set of intensities of the second fluorescent emission of light at a plurality of different respective emission wavelengths; and inferring a classification of the biological tissue or a concentration of a substance in the biological tissue with a multi-layer neural network or other machine learning model.