Explainable AI For Retinal Pathology Detection In OCT Images

Abstract

Diabetic macular edema (DME) and Age-Related Macular Degeneration (AMD) are two of the most common disorders that can cause blindness in a population and primarily cause retinal degradation. The application of multiple deep learning algorithms on Optical Coherence Tomography OCT) images to detect these disorders demonstrates excellent performance. However, because these algorithms include black box features, medical professionals are hesitant to fully trust the results. To address these challenges, we present a modified convolutional neural network based on the xception architecture for diagnosing DME and AMD using optical coherence tomography (OCT) images. To demonstrate the model’s transparency and trustworthiness, we used the Grad-CAM technique, which incorporates Explainable AI into the research and improves model interpretability. This technique assists medical specialists in demystifying deep learning algorithms and obtaining more information about the critical areas in OCT images used for prediction. The proposed model achieved an accuracy of 99.87%, a precision of 99.67%, and a recall of 98.29% on a dataset of 934 images.