MADET: A Novel Hybrid Network with EFU-Net and RMSNet for Retinal Microaneurysm Detection

Retinal microaneurysms (MA) are the earliest clinical indicators of diabetic retinopathy (DR), and their detection is crucial for early diagnosis of DR and preventing vision loss. However, accurately detecting MA is extremely challenging. Existing object detection models fail to accurately detect MA and suffer from severe under-detection, because MA are characterized by tiny size and low contrast with the fundus background. To address this challenge, we have developed a two-stage learning model, MADET, which is proposed. MADET consists of two models: an enhanced feature attention fusion model (EFU-Net) for segmentation and a residual multi-scale model (RMSNet) for classification. EFU-Net integrates multi-attention gate fusion (MAGF) modules and residual RFCBAM convolution (ResRF-conv) into the standard U-Net architecture to improve the encoder-decoder connection and enhance the model’s ability to acquire strong context features, resulting in better segmenting of the MA regions. RMSNet considers the difference of feature information between different scales to eliminate non-MA regions, thereby achieving more accurate MA detection. MADET achieved a Free-response Receiver Operating Characteristic (FROC) score of 0.625 on the E-Ophtha-MA dataset (148 images), showing an improvement of 0.034 over recent methods. On the IDRiD dataset (81 images), it achieved a score of 0.497, which is 0.021 higher than the latest comparable approach. These results indicate that MADET achieves competitive or near-best performance compared to existing state-of-the-art techniques.