Comparative Performance Analysis of Transformer-Based Pre-Trained Models for Detecting Keratoconus Disease

TL;DR

This study compares eight pre-trained CNN models for keratoconus detection, identifying MobileNetV2 as the most accurate, and highlights the strengths and weaknesses of each model in classifying various case types.

Contribution

It provides a comprehensive comparison of CNN architectures for keratoconus diagnosis, emphasizing model performance and areas needing refinement.

Findings

MobileNetV2 achieved highest accuracy in keratoconus detection.

InceptionV3 and DenseNet121 performed well but struggled with questionable cases.

EfficientNetB0, ResNet50, and VGG19 had difficulty distinguishing dubious cases.

Abstract

This study compares eight pre-trained CNNs for diagnosing keratoconus, a degenerative eye disease. A carefully selected dataset of keratoconus, normal, and suspicious cases was used. The models tested include DenseNet121, EfficientNetB0, InceptionResNetV2, InceptionV3, MobileNetV2, ResNet50, VGG16, and VGG19. To maximize model training, bad sample removal, resizing, rescaling, and augmentation were used. The models were trained with similar parameters, activation function, classification function, and optimizer to compare performance. To determine class separation effectiveness, each model was evaluated on accuracy, precision, recall, and F1-score. MobileNetV2 was the best accurate model in identifying keratoconus and normal cases with few misclassifications. InceptionV3 and DenseNet121 both performed well in keratoconus detection, but they had trouble with questionable cases. In contrast, EfficientNetB0, ResNet50, and VGG19 had more difficulty distinguishing dubious cases from regular ones, indicating the need for model refining and development. A detailed comparison of state-of-the-art CNN architectures for automated keratoconus identification reveals each model's benefits and weaknesses. This study shows that advanced deep learning models can enhance keratoconus diagnosis and treatment planning. Future research should explore hybrid models and integrate clinical parameters to improve diagnostic accuracy and robustness in real-world clinical applications, paving the way for more effective AI-driven ophthalmology tools.