Work

Dilated Neural Networks for Improving Microaneurysm Detection in Fundus Images

Year: 2025

Type: article

Abstract: Diabetic retinopathy (DR) is a microvascular complication that can lead to blindness if left untreated. Early detection of microaneurysms (MAs) is crucial for effective DR diagnosis. However, detecting MAs is challenging due to their small size, low contrast, and the presence of noise and other artifacts. This paper presents a novel deep learning approach for MA detection using a dilated residual network (DRN) with an Region of interest (ROI) layer. Unlike traditional models such as VGG16 and ResNet, our DRN model achieves a higher input resolution (28 × 28) for ROI pooling, significantly improving MA detection accuracy. Extensive experiments confirm the effectiveness of our approach, achieving superior performance compared to state-of-the-art methods on the e-ophtha-MA, DiaretDB1-MA, and ROCh datasets. Notably, our method achieves the best FROC (free-response receiver operating characteristic) score of 0.6038 on the e-ophtha-MA dataset.

Source: IEEE Access

DOI: 10.1109/access.2025.3579342

Authors: Norah Asiri, Abdel Monim Artoli, Muna Al-Razgan, Taha Alfakih

Institution: King Saud University

Language: English

Topic: Retinal Imaging and Analysis

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,075