An Efficient and Light Weight Deep Learning Model for Accurate Retinal Vessels Segmentation

Abstract

Detecting eye diseases early can make a difference when trying to treat them. Existing diagnostic systems are not only prone to inaccurate judgments, but are also difficult and require a longer time from experts. Artificial intelligence (AI) based on deep learning (DL) has attracted global interest recently because of its effectiveness and accuracy in detecting eye diseases. There are several challenges in diagnosing eye diseases based on retinal fundus imaging. Most of the previous models in the literature targeted these challenges and tried to improve the evaluation metrics, whereas very little attention has been given to reducing the computational complexity of the developed model. The reduced computational complexity is highly desired, as the aim of developing an AI-enabled automated diagnostic system is to use for population-scale screening programs. This observation motivated us, and this work aimed to use a lightweight DL model based on ColonSegNet for retinal vessel segmentation. The performance of the model was assessed using three open-access fundus image datasets: DRIVE, CHASE_DB1, and STARE, and it achieved sensitivity, specificity, accuracy, AUC, and MCC performance of (0.8491, 0.9774, 0.9659, 0.9850, and 0.7960), (0.8607, 0.9806,0.9731, 0.9869, and 0.8014), and (0.8573, 0.9813, 0.9719, 0.9873, and 0.8069) respectively. Furthermore, the proposed method has five M trainable parameters, making it lightweight and capable of deployment on low-end hardware devices. These results outperform several lightweight and computationally heavy methods. The reduced number of parameters, computational complexity, and improved segmentation performance support its use in automated diagnostic systems for retinal vessel segmentation.

Keywords

• Deep learning
• diabetic retinopathy
• image segmentation
• glaucoma
• light weight