Autoantibody Immunofluorescence Image Classification via CNN
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Solution Overview
Problem
Current methods for interpreting immunofluorescence images of autoantibodies are labor-intensive and inefficient, requiring manual processing and pre-processing steps, which hinders accurate prediction of extractable nuclear antigens and corresponding disease types.
Innovation Solution
A classification system and method using convolutional neural networks (CNNs) that directly analyze original cell immunofluorescence images, converting them into three primary color layers and employing convolution, pooling, and inception layers to capture features, fully connecting them with extractable nuclear antigen results to establish classification models for predicting antigen types and disease associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation methods are used for immunofluorescence images, then interpretation accuracy can be maintained through expert judgment, but labor demand and time consumption increase significantly
Solution Approach 1:
The patent replaces manual mechanical interpretation with an automated computer-based system that uses image processing algorithms and machine learning models to analyze immunofluorescence images, thereby substituting human labor with automated computational methods while maintaining diagnostic accuracy
Solution Approach 2:
The system enables self-service interpretation where the computer automatically processes and interprets immunofluorescence images without requiring manual intervention, allowing the system to serve itself in performing the complete interpretation workflow from image input to diagnostic output
2Measurement precision
If pre-processing steps such as cell boundary cutting and pixel blurring are applied before machine interpretation, then interpretation effectiveness may be improved, but the number of processing steps increases and interpretation efficiency decreases
Solution Approach 1:
The patent extracts and removes the unnecessary pre-processing steps (cell boundary cutting, pixel blurring) from the interpretation workflow, retaining only the essential image input and machine learning analysis steps, thereby simplifying the overall process while maintaining effectiveness
Solution Approach 2:
The machine learning model is designed to handle multiple functions including image normalization, feature extraction, and classification within a single unified framework, eliminating the need for separate pre-processing steps and reducing overall system complexity
3Loss of information
If current pre-processing methods are used, then some interpretation results can be obtained, but the system cannot directly link to blood examination results of extractable nuclear antigens
Solution Approach 1:
The patent merges the image interpretation process with the extractable nuclear antigen classification by integrating multiple classification models that simultaneously analyze immunofluorescence images and predict both image patterns and corresponding antigen types, thereby combining previously separate functions into a unified system
Solution Approach 2:
The system performs preliminary classification of extractable nuclear antigens during the image interpretation process itself, rather than requiring separate subsequent steps, thereby anticipating and preparing the linkage to blood examination results before they are needed
Data Source
AI summary
A classification system and a classification method of the autoantibody immunofluorescence image are disclosed. The system includes an input device, a processor and an output device. The plurality of cell immunofluorescence images and the corresponded extractable nuclear antigen results are input through the input device. The processor conducts a plurality of convolution neural network calculation and output the classification based on the extractable nuclear antigen results, so as to obtain an extractable nuclear antigen classification model. When the autoantibody immunofluorescence image is input by the input device, the corresponded classification of the extractable nuclear antigen can be predicted. The classification result is output through the output device.