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Advancements in Cataract Detection: The Systematic Development of LeNet-Convolutional Neural Network Models.

Thittaporn Ganokratanaa¹, Mahasak Ketcham², Patiyuth Pramkeaw³

¹Applied Computer Science Programme, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand.

Journal of Imaging

|October 27, 2023

Summary

This study introduces a new AI tool for early cataract detection using smartphone images. The LeNet-CNN model achieves 96% accuracy, enabling faster screening and treatment for this leading cause of blindness.

Keywords:

LeNet–convolutional neural network cataract detection systematic development

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Area of Science:

Ophthalmology
Computer Science
Medical Imaging

Background:

Cataracts are a leading cause of blindness globally and in Thailand.
Delayed patient treatment seeking is a significant barrier to managing cataracts effectively.
Current screening methods may not be readily accessible or convenient for early detection.

Purpose of the Study:

To develop and evaluate an AI-based system for preliminary cataract abnormality identification.
To compare the performance of LeNet-convolutional neural network (LeNet-CNN) with support vector machine (SVM) for cataract classification.
To create a tool for accessible, self-administered eye health assessment.

Main Methods:

Utilized image processing techniques and machine learning for cataract detection.
Trained a LeNet-CNN model on a dataset of digital camera images.
Compared LeNet-CNN performance against a support vector machine (SVM) model.

Main Results:

The LeNet-CNN model achieved 96% accuracy in identifying cataract abnormalities.
Sensitivity was 95% for positive cases, and specificity was 96% for negative cases.
LeNet-CNN performance surpassed previous studies, demonstrating high accuracy and effectiveness.

Conclusions:

The proposed LeNet-CNN approach offers an accurate and effective tool for initial cataract screening.
Image processing and convolutional neural networks can significantly enhance preliminary cataract assessment.
This technology facilitates early intervention by enabling patients to perform self-assessments, improving access to timely medical care.