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Automated detection of mild and multi-class diabetic eye diseases using deep learning
Rubina Sarki1, Khandakar Ahmed1, Hua Wang1
1Victoria University, Ballarat Road, Melbourne, VIC 3011 USA.
Health Information Science and Systems
|October 22, 2020
Summary
Automated deep learning models can classify diabetic eye disease (DED) from retinal images. This research developed a system achieving up to 88.3% accuracy for multi-class DED detection.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Diabetic eye disease (DED) poses a significant risk of vision impairment.
- Early detection through retinal fundus image screening is crucial for timely treatment.
- Manual analysis of retinal images is time-consuming and labor-intensive for ophthalmologists.
Purpose of the Study:
- To develop an automated classification system for diabetic eye disease using deep learning.
- To address the challenge of classifying mild and multi-class DED.
- To reduce the diagnostic workload and time for ophthalmologists.
Main Methods:
- Utilized pre-trained Convolutional Neural Network (CNN) models (VGG16) on ImageNet for image classification.
- Applied performance enhancement techniques including fine-tuning, optimization, and contrast enhancement.
- Tested the models on ophthalmologist-annotated retinal fundus image datasets.
Main Results:
- Achieved a maximum accuracy of 88.3% for multi-class DED classification using the VGG16 model.
- Attained 85.95% accuracy for mild multi-class DED classification.
- Demonstrated the effectiveness of deep learning in automated DED screening.
Conclusions:
- Automated deep learning systems show promise for efficient and accurate diabetic eye disease diagnosis.
- The developed system can aid ophthalmologists in faster and more reliable DED detection.
- Further research can refine these models for broader clinical application.
