Abstract
Microaneurysms (MAs) are the earliest detectable diabetic retinopathy (DR) lesions. Thus, the ability to automatically detect MAs is critical for the early diagnosis of DR. However, achieving the accurate and reliable detection of MAs remains a significant challenge due to the size and complexity of retinal fundus images. Therefore, this paper presents a novel MA detection method based on a deep neural network with a multilayer attention mechanism for retinal fundus images. First, a series of equalization operations are performed to improve the quality of the fundus images. Then, based on the attention mechanism, multiple feature layers with obvious target features are fused to achieve preliminary MA detection. Finally, the spatial relationships between MAs and blood vessels are utilized to perform a secondary screening of the preliminary test results to obtain the final MA detection results. We evaluated the method on the IDRiD_VOC dataset, which was collected from the open IDRiD dataset. The results show that our method effectively improves the average accuracy and sensitivity of MA detection.
Highlights
Automated medical data processing is a current trend in bioinformatics analysis that is beneficial for disease detection and diagnosis
We first compared existing object detection algorithms with the attention-based feature fusion method proposed in this paper and compared the fusion results of schemes involving different layers based on the attention mechanism
The learning rate represents the speed of parameter updating; the momentum is the weight of the previous gradient update during the gradient update process and protects the model from both the disappearing and exploding gradient problems
Summary
Automated medical data processing is a current trend in bioinformatics analysis that is beneficial for disease detection and diagnosis. Diabetic retinopathy (DR) is one of the most serious complications of diabetes mellitus and is a major cause of blindness worldwide [1,2]. No apparent clinical symptoms exist during the early stage of DR, but the disease causes fine lesions to form on the retina and eventually leads to blindness. Early diagnosis and treatment of DR are highly important. The existing DR screening method relies on professional ophthalmologists to assess colour retinal fundus images, a procedure that requires a high level of professional expertise and has low screening efficiency. As the incidence of diabetes increases, the necessary medical resources are becoming increasingly scarce
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