Deep learning framework for neurological diseases diagnosis through near-infrared eye video and time series imaging algorithms

Abstract

Internet of Things (IoT) solutions have greatly evolved recently with the application of Artificial Intelligence (AI). Indeed, AI enriches the IoT with intelligence capabilities. In particular, AI methods are highly effective in the scope of the Internet of Medical Things (IoMT) for the applications requiring decision support for doctors. In this work, we propose a Deep Learning (DL) framework for the classification of Parkinson’s disease (PD) and Progressive Supranuclear Palsy (PSP). In contrast to the state-of-the-art solutions relying on only the saccade test for the classification of these neurodegenerative diseases, we collect a dataset while the subjects perform five exercises including saccade, spontaneous nystagmus, optokinetic nystagmus, pursuit, and gaze test. We then extract the pupil features (coordinates, area, minor axis, and major axis) using the image segmentation DL model and represent them as images using Gramian Angular Difference Field (GADF) time series imagining algorithm. The resultant images are supplied to the proposed disease-detection model for running the classification procedure. The best classification results were obtained for the optokinetic exercise with the accuracy of 96.9%, 90.8%, and 96.9% for the left, right, and both eyes, respectively.