Identify Type of Squint of Human's Eye through Deep Network EfficientNet-B0 with Grad-CAM

Wafaa H. Alwan^1,*, Sabah M. Imran²

¹College of Computer Science & Information Technology ,Karbala University, Iraq

² College of Law ,Karbala University, Iraq

Emails: Wefaa.hesen@uokerbala.edu.iq; Sabah.m@uokerbala.edu.iq

Abstract

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.

Keywords: Strabismus classification; Deep learning; EfficientNet-B0; Squeeze-and-Excitation; Medical diagnostics; Image classification