Automatic Detection of Microaneurysms in Fundus Images

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Author(s): Jesús Eduardo Ochoa Astorga (Kyoto Institute of Technology, Japan), Linni Wang (Tianjin Medical University Eye Hospital, China), Shuhei Yamada (Kyoto Institute of Technology, Japan), Yusuke Fujiwara (Kyoto Institute of Technology, Japan), Weiwei Du (Kyoto Institute of Technology, Japan)and Yahui Peng (Beijing Jiaotong University, China)
Copyright: 2023
Volume: 11
Issue: 1
Pages: 14
Source title: International Journal of Software Innovation (IJSI)
Editor(s)-in-Chief: Roger Y. Lee (Central Michigan University, USA)and Lawrence Chung (The University of Texas at Dallas, USA)
DOI: 10.4018/IJSI.315658

Keywords: Computer Science & IT / Information Science Reference / Systems & Software Design / Systems and Software Engineering

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Abstract

Early detection and treatment of diabetic retinopathy can delay blindness and improve quality of life for diabetic patients. It is difficult to detect early symptoms of diabetic retinopathy, which is presented by few microaneurysms in fundus images. This study proposes an algorithm to detect microaneurysms in fundus images automatically. The proposal includes microaneurysms segmentation by U-Net model and their false positives removal by ResNet model. The effectiveness of the proposal is evaluated with the public database IDRiD and E-ophtha by the area under precision recall curve (AUPR). 90% of microaneurysms can be detected at early stages of diabetic retinopathy. This proposal outperforms previous methods based in AUPR evaluation.

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Automatic Detection of Microaneurysms in Fundus Images

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