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Updated: Sep 6, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Optical coherence tomography image based eye disease detection using deep convolutional neural network.

Puneet¹, Rakesh Kumar¹, Meenu Gupta¹

¹Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab India.

Health Information Science and Systems

|June 27, 2022

Summary

This summary is machine-generated.

This study introduces an AI model using Deep Convolution Neural Networks for accurate eye disease diagnosis from Optical Coherence Tomography images. The model achieves high accuracy, aiding ophthalmologists in screening conditions like Diabetic Macular Edema.

Keywords:

Artificial intelligence Convolutional neural network Deep learning (DL)Diabetic retinopathy Eye disease Ophthalmology Optical coherence tomography Transfer learning

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Traditional eye disease diagnosis faces challenges due to technological limitations and manual processes, leading to time consumption and potential errors.
Existing autonomous systems for disease categorization often lack state-of-the-art accuracy.
Computer science technologies like Artificial Intelligence (AI) are increasingly vital in medical diagnostics, particularly in ophthalmology.

Purpose of the Study:

To develop an accurate and efficient autonomous system for classifying ocular disorders using deep learning.
To reduce the diagnostic burden on ophthalmologists by providing a reliable screening tool.

Main Methods:

Implementation of Attention mechanisms and Transfer Learning with Deep Convolution Neural Networks (CNNs).
Utilizing Optical Coherence Tomography (OCT) images for disease classification.
Training and testing the model on datasets for specific ocular conditions.

Main Results:

The proposed AI model achieved a training accuracy of 97.79% and a testing accuracy of 95.6%.
The model demonstrated efficient classification of Choroidal Neovascularization, Diabetic Macular Edema, and Drusen from OCT images.
The system shows potential for practical application in healthcare settings.

Conclusions:

The developed AI model offers a highly accurate and automated solution for diagnosing specific eye diseases.
This approach can significantly assist in the early screening of conditions like Diabetic Retinopathy, reducing the workload for ophthalmologists.