MHANet: A hybrid attention mechanism for retinal diseases classification

Author:

Xu Lianghui,Wang LiejunORCID,Cheng Shuli,Li Yongming

Abstract

With the increase of patients with retinopathy, retinopathy recognition has become a research hotspot. In this article, we describe the etiology and symptoms of three kinds of retinal diseases, including drusen(DRUSEN), choroidal neovascularization(CNV) and diabetic macular edema(DME). In addition, we also propose a hybrid attention mechanism to classify and recognize different types of retinopathy images. In particular, the hybrid attention mechanism proposed in this paper includes parallel spatial attention mechanism and channel attention mechanism. It can extract the key features in the channel dimension and spatial dimension of retinopathy images, and reduce the negative impact of background information on classification results. The experimental results show that the hybrid attention mechanism proposed in this paper can better assist the network to focus on extracting thr fetures of the retinopathy area and enhance the adaptability to the differences of different data sets. Finally, the hybrid attention mechanism achieved 96.5% and 99.76% classification accuracy on two public OCT data sets of retinopathy, respectively.

Funder

the Natural Science Foundation of Xinjiang Uygur Autonomous Region

Tianshan Innovation Team of Xinjiang Uygur Autonomous Region

National Science Foundation of China

Creative Research Groups of Higher Education of Xinjiang Uygur Autonomous Region

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

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