Affiliation:
1. Department of Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
2. Shen Yuan Honors College, Beihang University, Beijing 100191, China
3. Department of Ophthalmology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
Abstract
Retinopathy, a prevalent disease causing visual impairment and sometimes blindness, affects many individuals in the population. Early detection and treatment of the disease can be facilitated by monitoring the retina using fundus imaging. Nonetheless, the limited availability of fundus images and the imbalanced datasets warrant the development of more precise and efficient algorithms to enhance diagnostic performance. This study presents a novel online knowledge distillation framework, called CLRD, which employs a collaborative learning approach for detecting retinopathy. By combining student models with varying scales and architectures, the CLRD framework extracts crucial pathological information from fundus images. The transfer of knowledge is accomplished by developing distortion information particular to fundus images, thereby enhancing model invariance. Our selection of student models includes the Transformer-based BEiT and the CNN-based ConvNeXt, which achieve accuracies of 98.77% and 96.88%, respectively. Furthermore, the proposed method has 5.69–23.13%, 5.37–23.73%, 5.74–23.17%, 11.24–45.21%, and 5.87–24.96% higher accuracy, precision, recall, specificity, and F1 score, respectively, compared to the advanced visual model. The results of our study indicate that the CLRD framework can effectively minimize generalization errors without compromising independent predictions made by student models, offering novel directions for further investigations into detecting retinopathy.
Funder
National Natural Science Foundation of China
Reference35 articles.
1. The impact of chronic diseases on depressive symptoms among the older adults: The role of sleep quality and empty nest status;Zhang;J. Affect. Disord.,2022
2. Sebastian, A., Elharrouss, O., Al-Maadeed, S., and Almaadeed, N. (2023). A Survey on Deep-Learning-Based Diabetic Retinopathy Classification. Diagnostics, 13.
3. Nijalingappa, P., and Sandeep, B. (2015, January 29–31). Machine learning approach for the identification of diabetes retinopathy and its stages. Proceedings of the 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Davangere, India.
4. Detection of Diabetic Retinopathy using Splat Feature Classification in Fundus Image;Latha;Int. J. Comput. Appl.,2015
5. An exudate detection method for diagnosis risk of diabetic macular edema in retinal images using feature-based and supervised classification;Marin;Med. Biol. Eng. Comput.,2018