Dataset

Infectious keratitis is a group of corneal disorders in which corneal tissues suffer inflammation and damage caused by pathogenic infections. Among these, Fungal Keratitis (FK) and Acanthamoeba Keratitis (AK) are some of the most severe, with a high chance of permanent blindness if they are not diagnosed early and accurately. In Vivo Confocal Microscopy (IVCM) allows the imaging of different layers of the cornea, providing an important tool for an early and more accurate diagnosis. In this paper, we introduce the IVCM-Keratitis dataset consisting of a total of 4001 sample images of AK and FK, as well as Non-Specific Keratitis (NSK) and healthy corneas classes. We also use this dataset to develop multiple deep-learning models based on Convolutional Neural Networks (CNNs) for the automated diagnosis of infectious keratitis. Among these models, Densenet161, had the best performance with accuracy, precision, recall, and F1 score of 93.55%, 92.52%, 94.77%, and 96.93%, respectively. These results demonstrate the potential of deep learning-based models in early and automated diagnosis of AK and FK. We further showed that these models can be used to highlight the areas of infection in the IVCM images and explain the reason behind their diagnosis by utilizing saliency maps as a technique used in eXplainable Artificial Intelligence (XAI) to interpret these models.