Comparative Analysis of Deep Learning Models for Enhanced Diagnosis of Keratoconus: Unveiling the Efficacy of MobileNetV2, VGG16, and InceptionV3

Abstract

This study aims to explore the efficacy of convolutional neural networks (CNNs), a deep learning methodology, in the early diagnosis of keratoconus, a severe corneal disorder with potential to lead to blindness. Predominantly manifesting in the second decade of life, keratoconus is a non-discriminatory disease affecting individuals across all sexes and races. The focus of this research was to assess and compare the diagnostic capabilities of three distinct deep CNN architectures: MobileNetV2, InceptionV3, and VGG16, in identifying keratoconusrelated pathologies. Through rigorous experimental analysis, it was observed that the MobileNetV2 model demonstrated superior performance in detecting keratoconus, achieving an impressive accuracy rate of 97.94%. This rate not only surpasses that of its D-CNN counterparts but also underscores the model's stability, robustness, and potential for real-world application in precise keratoconus identification. Further, the InceptionV3 D-CNN model exhibited exceptional performance in terms of precision, recall rates, and F1 scores. These metrics significantly endorse the model’s utility as an effective diagnostic tool, emphasizing its ability to accurately identify keratoconus cases while minimizing the incidence of false positives and negatives. The findings from this study highlight the practical applicability and reliability of the MobileNetV2 model in the early and accurate detection of keratoconus, showcasing its potential as a significant contribution to the field of ophthalmological diagnostics.