Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images

TL;DR

Optic-Net is a new convolutional neural network that accurately and efficiently diagnoses retinal diseases from SD-OCT images, outperforming existing models and human experts with near-perfect accuracy and real-time prediction.

Contribution

This paper introduces a novel CNN architecture that improves accuracy and computational efficiency in retinal disease diagnosis from SD-OCT images, addressing gradient explosion issues.

Findings

Achieved 99.8% and 100% accuracy on two datasets.

Outperformed other classification models and human diagnosticians.

Operates in real time for practical clinical use.

Abstract

Diagnosing different retinal diseases from Spectral Domain Optical Coherence Tomography (SD-OCT) images is a challenging task. Different automated approaches such as image processing, machine learning and deep learning algorithms have been used for early detection and diagnosis of retinal diseases. Unfortunately, these are prone to error and computational inefficiency, which requires further intervention from human experts. In this paper, we propose a novel convolution neural network architecture to successfully distinguish between different degeneration of retinal layers and their underlying causes. The proposed novel architecture outperforms other classification models while addressing the issue of gradient explosion. Our approach reaches near perfect accuracy of 99.8% and 100% for two separately available Retinal SD-OCT data-set respectively. Additionally, our architecture predicts retinal diseases in real time while outperforming human diagnosticians.