Automatic multi-disease classification on retinal images using multilevel glowworm swarm convolutional neural network

Abstract

In ophthalmology, early fundus screening is an economical and effective way to prevent blindness from eye diseases. Because clinical evidence does not exist, manual detection is time-consuming and may cause the situation to be delayed clinically. With the development of deep learning, a wide variety of eye diseases have shown promising results; however, most of these studies focus on only one disease. Therefore, focusing on multi-disease classification based on fundus images is an effective approach. Consequently, this paper presents a method based on the multilevel glowworm swarm optimization convolutional neural network (MGSCNN) for the classification of multiple diseases. It is proposed that the proposed system has two stages, namely preprocessing and classification. In the beginning, the images are normalized, smoothed, and resized to prepare them for preprocessing. After pre-processing, the images are fed to the MGSCNN classifier to classify an image as normal or abnormal (covering 39 different types of diseases). In the CNN classifier, with the help of Glowworm Swarm Optimizer (GSO), we optimally detect the structure and hyperparameters of CNN simultaneously. This approach achieves an excellent accuracy of 95.09% based on various metrics.