A lightweight deep learning model for retinal optical coherence tomography image classification

Abstract

AbstractAutomated retinal disease detection and grading is one of the most researched areas in medical image analysis. In recent years, Deep Learning models have attracted much attention in this field. Hence, in this paper, we present a Deep Learning‐based, lightweight, fully automated end‐to‐end diagnostic system for the detection of the two major retinal diseases, namely diabetic macular oedema (DME) and drusen macular degeneration (DMD). Early detection of these diseases is important to prevent vision impairment. Optical coherence tomography (OCT) is the main imaging technique for detecting these diseases. The model proposed in this work is based on residual blocks and channel attention modules. The performance of the model is evaluated using the publicly available Mendeley OCT dataset and the Duke dataset. We were able to achieve a classification accuracy of 99.5% in the Mendeley test dataset and 94.9% in the Duke dataset with the proposed model. For the application, we performed an extensive evaluation of pre‐trained models (LeNet, AlexNet, VGG‐16, ResNet50 and SE‐ResNet). The proposed model has a much smaller number of trainable parameters and shows superior performance compared to existing methods.