Diagnosis and detection of diabetic retinopathy based on transfer learning

• Published in: Multimedia tools and applications 2024-03, Vol.83 (35), p.82945-82961
• Main Authors: Liu, Kailai, Si, Ting, Huang, Chuanyi, Wang, Yiran, Feng, Huan, Si, Jiarui
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial neural networks; Cardiovascular disease; Computer Communication Networks; Computer Science; Data augmentation; Data Structures and Information Theory; Diabetes; Diabetes mellitus; Diabetic retinopathy; Diagnosis; Gray scale; Heart diseases; Hemorrhage; Image contrast; Image enhancement; Machine learning; Medical imaging; Methods; Multimedia Information Systems; Performance evaluation; Retinal images; Special Purpose and Application-Based Systems; Track 2: Medical Applications of Multimedia
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-024-18792-x

Diabetes Mellitus (DM) is a chronic condition that affects the blood glucose metabolism of various organs and tissues throughout the body. It can result in microvascular disorders such as coronary heart disease and cerebral hemorrhage. One significant complication is retinopathy, which, in severe cases, can lead to blindness. Early screening and detection are crucial as the disease process is irreversible. In this study, we developed a model for early screening of Diabetes Retinopathy (DR) using color fundus photography images. Our approach involved employing CLAHE, grayscale image transformation methods, and transfer learning to improve diagnostic efficiency when working with limited data. The APTOS 2019 dataset, consisting of3662 retinal images, was used in this research. Four different preprocessing methods were applied to the retinal images, including removing the black edge, resizing, and normalization (Method I), adding contrast constrained adaptive histogram equalization (CLAHE) to Method I (Method II), adding grayscale transformation to Method I (Method III), and adding CLAHE and grayscale transformations to Method I (Method IV). Data augmentation techniques such as random brightness and contrast transformations, flipping, image cropping, and mix-up algorithms were utilized for data enhancement. The ResNet50 and InceptionV3 models based on convolutional neural networks were employed to build the model for learning retinal images under three scenarios: (1) learning from scratch, (2) transfer learning with fixed weights and training only the fully connected layer, and (3) transfer learning with loaded weights, followed by fine-tuning of the entire network based on the input data. The classification performance of the models was evaluated using metrics such as AUC, accuracy, F1 score, precision, and recall. For the ResNet50 model, the accuracy rates for learning from scratch, fixed weight, and fine-tuning weight were 75.41%, 54.64%, and 81.97%, respectively. When using the InceptionV3 model, the accuracy rates were 76.50%, 10.38%, and 83.61%, respectively. Fine-tuning was conducted on data II, III, and IV using the InceptionV3 model, resulting in accuracies of 81.42%, 80.87%, and 83.61%, respectively. Comparisons between models using the same data and training methods revealed that models employing the InceptionV3 structure achieved higher accuracy than those using ResNet50 (83.61% vs. 81.97%). The results indicate that the InceptionV3-based CNN, coup