Open AccessDIABETIC RETINOPATHY IMAGE CLASSIFICATION USING DEEP NEURAL NETWORK
Author(s) -
Parvathy En,
Bharadwaja Kumar G
Publication year - 2017
Publication title -
asian journal of pharmaceutical and clinical research
Language(s) - English
Resource type - Journals
eISSN - 2455-3891
pISSN - 0974-2441
DOI - 10.22159/ajpcr.2017.v10s1.20512
Subject(s) - convolutional neural network , artificial intelligence , computer science , deep learning , fundus (uterus) , diabetic retinopathy , field (mathematics) , task (project management) , image (mathematics) , artificial neural network , retinopathy , contextual image classification , pattern recognition (psychology) , computer vision , optometry , machine learning , medicine , ophthalmology , diabetes mellitus , mathematics , engineering , systems engineering , pure mathematics , endocrinology
Healthcare is an important field where image classification has an excellent value. An alarming healthcare problem recognized by the WHO that theworld suffers is diabetic retinopathy (DR). DR is a global epidemic which leads to the vision loss. Diagnosing the disease using fundus images is a timeconsuming task and needs experience clinicians to detect the small changes. Here, we are proposing an approach to diagnose the DR and its severity levels from fundus images using convolutional neural network algorithm (CNN). Using CNN, we are developing a training model which identifies the features through iterations. Later, this training model will classify the retina images of patients according to the severity levels. In healthcare field, efficiency and accuracy is important, so using deep learning algorithms for image classification can address these problems efficiently.