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Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods.

Md Kamrul Hasan¹, Tanjum Tanha¹, Md Ruhul Amin¹

¹Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh.

Computational and Mathematical Methods in Medicine

|December 20, 2021

Summary

This summary is machine-generated.

Early detection of cataracts, a common age-related vision disorder, is crucial. This study successfully classified cataracts using InceptionResnetV2 convolutional neural networks, achieving high accuracy and aiding in blindness prevention.

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Area of Science:

Ophthalmology
Computer Science
Artificial Intelligence

Background:

Cataracts are a leading cause of visual impairment in aging populations.
Symptoms include blurred vision, faded colors, and light sensitivity, impacting daily activities.
Early detection and prevention are vital to reduce blindness rates.

Purpose of the Study:

To classify cataract disease using convolutional neural networks (CNNs).
To evaluate the performance of different CNN meta-architectures for cataract detection.
To establish an accurate and efficient method for early cataract identification.

Main Methods:

Utilized a publicly available image dataset for cataract classification.
Applied four CNN meta-architectures: InceptionV3, InceptionResnetV2, Xception, and DenseNet121.
Employed the TensorFlow object detection framework for model implementation.

Main Results:

The InceptionResnetV2 model achieved state-of-the-art performance.
Achieved a training accuracy of 99.54% and validation accuracy of 98.17%.
Demonstrated high sensitivity (96.55%) and perfect specificity (100%) in cataract detection.

Conclusions:

Convolutional neural networks, particularly InceptionResnetV2, are highly effective for cataract classification.
The developed model shows significant potential for early and accurate cataract detection.
This approach can contribute to minimizing blindness caused by cataracts.