Automated strabismus detection and classification using deep learning analysis of facial images

Strabismus, or eye misalignment, is a common condition affecting individuals of all ages. Early detection and accurate classification are essential for proper treatment and avoiding long-term complications. This research presents a new deep-learning-based approach for automatically identifying and classifying strabismus from facial images. The proposed methodology leverages Convolutional Neural Networks (CNNs) to achieve high accuracy in both binary (strabismus vs. normal) and multi-class (eight-class deviation angle for esotropia and exotropia) classification tasks. The dataset for binary classification consisted of 4,257 facial images, including 1,599 normal cases and 2,658 strabismus cases, while the multi-class classification involved 480 strabismic and 142 non-strabismic images. These images were labeled based on ophthalmologist measurements using the Alternate Prism Cover Test (APCT) or the Modified Krimsky Test (MK). Five-fold cross-validation was employed, and performance was evaluated using sensitivity, accuracy, F1-score, and recall metrics. The proposed deep learning model achieved an accuracy of 86.38% for binary classification and 92.7% for multi-class classification. These results demonstrate the potential of our approach to assist healthcare professionals in early strabismus detection and treatment planning, ultimately improving patient outcomes.