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Using Retinal Imaging to Study Dementia

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A Deep Learning-Based Framework for Retinal Disease Classification.

Amit Choudhary¹, Savita Ahlawat², Shabana Urooj³

¹University School of Automation and Robotics, G.G.S. Indraprastha University, New Delhi 110092, India.

Healthcare (Basel, Switzerland)

|January 21, 2023

Summary

This study introduces an AI model using VGG-19 architecture and transfer learning for automatic retinal disease detection from OCT images. The model achieves high accuracy in classifying four common retinal conditions.

Keywords:

VGG-19 architecture artificial intelligence diseased state of retina image processing neural networks performance analysis transfer learning

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Area of Science:

Ophthalmology
Artificial Intelligence
Medical Imaging

Background:

Accurate and early detection of retinal diseases is crucial for effective treatment.
Current diagnostic methods can be time-consuming and require specialized expertise.

Purpose of the Study:

To develop and evaluate an artificial intelligence model for the automatic detection and classification of retinal diseases.
To utilize deep learning, specifically the VGG-19 architecture with transfer learning, for analyzing optical coherence tomography (OCT) images.

Main Methods:

A customized 19-layer deep convolutional neural network (VGG-19 architecture) was employed.
Transfer learning was utilized to enhance the model's learning capabilities.
The model was trained on a dataset of 84,568 OCT retinal images across four classes: choroidal neovascularization, drusen, diabetic macular edema, and normal.

Main Results:

The proposed VGG-19 model achieved a classification accuracy of 99.17%.
The model demonstrated high performance with 0.995 specificity and 0.99 sensitivity.
Statistical evaluations, including ROC curve analysis and confusion matrix, confirmed the model's effectiveness.

Conclusions:

The VGG-19 architecture combined with transfer learning is a highly effective technique for automated retinal disease detection.
The developed AI model shows significant potential to improve the diagnostic process for retinal conditions.