Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
This addresses the challenge of automated, accurate diagnosis of retinal diseases for medical applications, though it appears incremental as it builds on existing deep learning methods for a specific domain.
The paper tackles the problem of diagnosing retinal diseases from SD-OCT images by proposing a novel CNN architecture, achieving near-perfect accuracies of 99.8% and 100% on two datasets and outperforming human diagnosticians in real-time.
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.