Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

• Published in: Entropy (Basel, Switzerland) Vol. 23; no. 7; p. 816
• Main Authors: Liu, Pingping, Yang, Xiaokang, Jin, Baixin, Zhou, Qiuzhan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.06.2021; MDPI
• Subjects: Artificial intelligence; Artificial neural networks; attention mechanism; bilinear pooling model; Classification; Datasets; Deep learning; Diabetes; Diabetes mellitus; Diabetic retinopathy; Disease; Entropy; Feature extraction; fine-grained image classification; Homology; Image classification; Image processing; Interdisciplinary subjects; Lesions; Machine learning; Medical imaging; Medical research; Neural networks; Patients; Physicians; Research methodology; Sample size; Support vector machines; Wavelet transforms; attention mechanism; fine-grained image classification; bilinear pooling model
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e23070816

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.