Automated identification of ocular toxoplasmosis in fundoscopic images utilizing deep learning models

The detection of ocular toxoplasmosis is based on eye fundus images, which are produced and analysed by a specialist optician. Although Deep Learning techniques are being quickly adopted in many areas, their application in ocular diagnosis has not been widely studied. In this research, we created a highly effective Convolutional Neural Network (CNN) model that can accurately identify and classify ocular Toxoplasmosis (OT) images into four categories: healthy, active, inactive, and active-inactive. After further examination, we merged three of these categories—active, inactive, and active-inactive—into a single class called "unhealthy," resulting in a binary classification of healthy and unhealthy images. Our ensemble model shows 96% accuracy. Our CNN model shows a high level of accuracy in distinguishing between these two categories. To validate the performance of our custom CNN model, we compared it with three pre-trained models (VGG16, VGG19, and MobileNet) using the same dataset. The findings revealed that both our proposed CNN model and the pre-trained architectures displayed comparable performance metrics, including accuracy, recall, F1 score, and precision. Our model achieved a remarkable accuracy of 97%, surpassing the performance of previously utilised models in diagnosing retinal disorders.