Diabetic Retinopathy Recognition Using Deep Learning Segmentation
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Solution Overview
Problem
Current methods for diagnosing diabetic retinopathy in fundus images require manual examination by ophthalmologists, which is time-consuming and not scalable, leading to many undiagnosed cases due to the high workload and limited access to eye exams.
Innovation Solution
A diabetic retinopathy recognition system using deep learning that processes fundus images through an image acquisition apparatus and automatic recognition apparatus, including pre-processing, neural networks, and feature combination to simulate a doctor's diagnosis and output diagnostic results, potentially using a handheld camera and cloud server for network communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual examination by ophthalmologists is used, then diagnostic accuracy is maintained, but workload increases and scalability decreases
Solution Approach 1:
An automatic recognition apparatus serves as an intermediary between fundus images and diagnostic conclusions. This apparatus pre-processes images, extracts features using neural networks, and generates preliminary diagnostic results, thereby reducing ophthalmologists' workload while maintaining diagnostic accuracy through their final verification
Solution Approach 2:
The diagnostic process is segmented into multiple stages: image acquisition, pre-processing, feature extraction by neural networks, and final diagnosis. This segmentation allows automated handling of routine tasks while preserving human expertise for critical decision-making
2Productivity
If deep learning automatic recognition is implemented, then productivity increases, but system complexity increases
Solution Approach 1:
The complex deep learning system is divided into modular components: image acquisition apparatus, pre-processing module, neural network modules for feature extraction, and result generation. Each module performs a specific function, making the overall complex system manageable and maintainable
Solution Approach 2:
Multiple neural network modules act as intermediaries between raw images and final diagnoses. These modules progressively transform data through feature extraction and combination, bridging the gap between complex processing and simple interpretability
3Measurement precision
If multiple neural network modules are used, then measurement precision improves, but device complexity increases
Solution Approach 1:
The neural network system is segmented into specialized modules: first neural network for initial feature extraction, second neural network for alternative feature extraction, and third neural network for feature combination and diagnosis. Each module has a specific function that contributes to overall precision
Solution Approach 2:
Multiple feature sets from different neural networks are merged and combined to form a comprehensive feature representation. This combination of multiple independent feature extraction paths enhances detection accuracy by capturing diverse lesion characteristics
Data Source
AI summary
Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus (1) configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus (2) configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.