Optimising CNN Architecture for Accurate Detection of Tessellated Retinal Disease Using Fundus Images

Eyes are one of the vital organs for human beings, which serve as a primary gateway to perceive the surroundings. An abnormal situation, namely tessellated eye, is commonly caused by myopia, which has a characteristic mosaic-like pattern that can cause early vision loss, particularly in infants and youngsters. This work contributes with the usage of a variety of deep learning models to diagnose tessellated and normal fundus images automatically which will improve early detection that can lead to the prevention of vision loss. This study uses a standard dataset of 732 annotated fundus images obtained from Mendeley, Kaggle and a local ophthalmology centre. It also uses a variety of Convolutional Neural Network (CNN) architectures, including VGG16, VGG19, ResNet50 and sequential models, that are experimented for determining the best model and examined. Initially, the fundus images are pre-processed and enhanced to improve model resilience. Out of all the architectures, ResNet50 outperformed as the best model, with an accuracy of 79.45%, while VGG16 with data augmentation reported the best accuracy of 90.8%. Grad-CAM (Gradient-weighted Class Activation Mapping), an Explainable Artificial Intelligence (XAI) mechanism, is used to create heatmaps for interpretability, emphasising spots and pathologies that contribute to the model’s experimentation and judgements. The outcomes of this research highlight potential models namely ResNet50 and augmented VGG16 for reliably diagnosing the fundus images as tessellated or normal. The study also seeks to serve as a platform for future investigation of classifying various automated retinal diseases.