Comparisonal study of Deep Learning approaches on Retinal OCT Image

TL;DR

This study compares deep learning models for detecting retinal diseases from OCT images, finding that MobileNetV2 achieves the highest accuracy and demonstrating the potential for automated diagnosis tools.

Contribution

It introduces a comparative analysis of four deep learning models for retinal disease detection from OCT images, highlighting MobileNetV2's superior performance.

Findings

MobileNetV2 achieved 99.17% accuracy.

Xception model closely followed with 99.07% accuracy.

Deep learning models can effectively automate retinal disease detection.

Abstract

In medical science, the use of computer science in disease detection and diagnosis is gaining popularity. Previously, the detection of disease used to take a significant amount of time and was less reliable. Machine learning (ML) techniques employed in recent biomedical researches are making revolutionary changes by gaining higher accuracy with more concise timing. At present, it is even possible to automatically detect diseases from the scanned images with the help of ML. In this research, we have taken such an attempt to detect retinal diseases from optical coherence tomography (OCT) X-ray images. Here, we propose a deep learning (DL) based approach in detecting retinal diseases from OCT images which can identify three conditions of the retina. Four different models used in this approach are compared with each other. On the test set, the detection accuracy is 98.00\% for a vanilla convolutional neural network (CNN) model, 99.07\% for Xception model, 97.00\% for ResNet50 model, and 99.17\% for MobileNetV2 model. The MobileNetV2 model acquires the highest accuracy, and the closest to the highest is the Xception model. The proposed approach has a potential impact on creating a tool for automatically detecting retinal diseases.