We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Diabetic Retinopathy Diagnosis Based on RA-EfficientNet.
- Authors
Yi, San-Li; Yang, Xue-Lian; Wang, Tian-Wei; She, Fu-Rong; Xiong, Xin; He, Jian-Feng
- Abstract
The early detection and grade diagnosis of diabetic retinopathy (DR) are very important for the avoidance of blindness, and using deep learning methods to automatically diagnose DR has attracted great attention. However, the small amount of DR data limits its application. To automatically learn the disease's features and detect DR more accurately, we constructed a DR grade diagnostic model. To realize the model, the authors performed the following steps: firstly, we preprocess the DR images to solve the existing problems in an APTOS 2019 dataset, such as size difference, information redundancy and the data imbalance. Secondly, to extract more valid image features, a new network named RA-EfficientNet is proposed, in which a residual attention (RA) block is added to EfficientNet to extract more features and to solve the problem of small differences between lesions. EfficientNet has been previously trained on the ImageNet dataset, based on transfer learning technology, to overcome the small sample size problem of DR. Lastly, based on the extracted features, two classifiers are designed, one is a 2-grade classifier and the other a 5-grade classifier. The 2-grade classifier can diagnose DR, and the 5-grade classifier provides 5 grades of diagnosis for DR, as follows: 0 for No DR, 1 for mild DR, 2 for moderate, 3 for severe and 4 for proliferative DR. Experiments show that our proposed RA-EfficientNet can achieve better performance, with an accuracy value of 98.36% and a kappa score of 96.72% in a 2-grade classification and an accuracy value of 93.55% and a kappa score of 91.93% in a 5-grade classification. The results indicate that the proposed model effectively improves DR detection efficiency and resolves the existing limitation of manual feature extraction.
- Subjects
DIABETIC retinopathy; DEEP learning; PROBLEM solving; DIAGNOSIS; FEATURE extraction; TECHNOLOGY transfer; MULTISPECTRAL imaging; MULTICASTING (Computer networks)
- Publication
Applied Sciences (2076-3417), 2021, Vol 11, Issue 22, p11035
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app112211035