BAM: Block attention mechanism for OCT image classification. Issue 5 (31st January 2022)

• Record Type: Journal Article
• Authors: Nabijiang, Maidina; Wan, Xinjuan; Huang, Shengsong; Liu, Qi; Wei, Bixia; Zhu, Jianing; Xie, Xiaodong
• Is Part Of: IET image processing. Volume 16:Issue 5(2022)
• Page Start: 1376
• Page End: 1388
• Publication Date: 2022-01-31
• Subjects: Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12415
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: Diabetic retinopathy attracts considerable research interest due to the number of diabetic patients increasing rapidly in recent years. Diabetic retinopathy is a common symptom of retinopathy, which damages the patient's eyesight and even causes the patient to lose sight. The authors propose a novel attention mechanism named block attention mechanism to actively explore the role of attention mechanisms in recognizing retinopathy features. Specifically, the block attention mechanism contributions are as follows: (1) The relationship between the blocks in the entire feature map is explored, and the corresponding coefficients are assigned to different blocks to highlight the importance of blocks. (2) Furthermore, the relationship between the edge elements of the feature map and the edge elements is explored, and corresponding coefficients are assigned to the elements at different positions on the feature map to highlight the importance of the elements in the feature map. Experimental results show that the proposed framework outperforms the existing popular attention‐based baselines on two public retina datasets, OCT2017 and SD‐OCT, achieving a 99.64% and 96.54% accuracy rate, respectively.