Optic-Net- A Novel Convolutional Neural Networkfor Diagnosis of Retinal Diseases from OpticalTomography Images
Abstract - Abstract—Diagnosing different retinal diseases from SpectralDomain Optical Coherence Tomography (SD-OCT) images is achallenging task. Different automated approaches such as imageprocessing, machine learning and deep learning algorithms havebeen used for early detection and diagnosis of retinal diseases.Unfortunately, these are prone to error and computationalinefficiency, which requires further intervention from humanexperts. In this paper, we propose a novel convolution neuralnetwork architecture to successfully distinguish between differentdegeneration of retinal layers and their underlying causes.The proposed novel architecture outperforms other classificationmodels while addressing the issue of gradient explosion. Ourapproach reaches near perfect accuracy of 99.8% and 100% fortwo separately available Retinal SD-OCT data-set respectively.Additionally, our architecture predicts retinal diseases in realtime while outperforming human diagnosticians.Keywords—SD-OCT, Convolutional Neural Networks, RetinalDegeneration; Residual Neural Network; Deep Learning; Com-puter Vision
Paper - https://arxiv.org/pdf/1910.05672v1.pdf
Dataset - https://www.kaggle.com/paultimothymooney/kermany2018