Open AccessBAM: Block attention mechanism for OCT image classification
Author(s) -
Nabijiang Maidina,
Wan Xinjuan,
Huang Shengsong,
Liu Qi,
Wei Bixia,
Zhu Jianing,
Xie Xiaodong
Publication year - 2022
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12415
Subject(s) - feature (linguistics) , diabetic retinopathy , block (permutation group theory) , mechanism (biology) , computer science , enhanced data rates for gsm evolution , artificial intelligence , retinopathy , pattern recognition (psychology) , image (mathematics) , damages , sight , medicine , mathematics , diabetes mellitus , philosophy , linguistics , geometry , epistemology , endocrinology , physics , astronomy , political science , law
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.