Advanced Image Enhancement and a Lightweight Feature Pyramid Network for Detecting Microaneurysms in Diabetic Retinopathy Screening

Abstract

Diabetic retinopathy (DR) is a complication of diabetes that can lead to vision impairment and even permanent blindness. The increasing number of diabetic patients and a shortage of ophthalmologists highlight the need for automated screening tools for early detection. Microaneurysms (MAs) are the earliest indicators of DR. However, detecting MAs in fundus images is a challenging task due to its small size and subtle features. Additionally, low contrast, noise, and lighting variations in fundus images, such as glare and shadows, further complicate the detection process. To address these challenges, we incorporated image enhancement techniques such as green channel utilization, gamma correction, and median filtering to improve image quality. Furthermore, to enhance the performance of the MA detection model, we employed a lightweight feature pyramid network (FPN) with a pretrained ResNet34 backbone to capture multiscale features and the convolutional block attention module (CBAM) to enhance feature selection. CBAM applies spatial and channel-wise attention, which allows the model to focus on the most relevant features for improved detection. We evaluated our method on the IDRID and E-ophtha datasets, achieving a sensitivity of 0.607 and F1 score of 0.681 on IDRID and a sensitivity of 0.602 and F1 score of 0.650 on E-ophtha. These experimental results show that our proposed method gives better results than previous methods.