Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3917 2018 2018 College of Computer Science & Information Technology, University of kerbala, Iraq Wafaa Wafaa College of Law, University of kerbala, Iraq Sabah M. Imran Finding and treating different types of strabismus, which is when the eyes do not line up properly, can be challenging. This study introduces a deep learning system that automatically identifies five types of strabismus: esotropia, exotropia, hypertropia, hypotropia, and normal eye alignment. It combines EfficientNet-B0 with Grad-CAM to improve how the system recognizes and classifies these conditions accurately. These help EfficientNet-B0 improve how it picks out important features using squeeze-and-excitation blocks, which capture key details needed for accurate classification. Grad-CAM further refines this process and localizes the critical regions in the feature maps more effectively to improve interpretability. We trained the model on a dataset of 10,000 balanced images across the five classes, achieving a classification accuracy of 99.43% and 96.33% for training and testing data, respectively. The model's focus-based architecture ensures that clinicians' set goals are met in terms of the model's efficiency and reliability for predictions. 2026 2026 01 12 10.54216/FPA.210101 https://www.americaspg.com/articleinfo/3/show/3917