Open AccessSBC Based Diabetic Retinopathy and Diabetic Macular Edema Classification System using Deep Convolutional Neural Network
Author(s) -
Anicia Coleen S. Reyes,
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Gem Ryan C. Milan,
James Marvin M. Quilaton,
Bryant Exel G. Sigue,
Steven Valentino E. Arellano,
Kenneth C. Karamihan,
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Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c4195.099320
Subject(s) - diabetic retinopathy , medicine , convolutional neural network , diabetic macular edema , ophthalmology , artificial intelligence , optometry , retinal , computer science , diabetes mellitus , endocrinology
This Raspberry Pi Single-Board Computer-Based Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) Classification System using Deep Convolutional Neural Network through Inception v3 Transfer Learning and MATLAB digital image processing paradigm based on International Clinical DR and DME Disease Severity Scale with Python application, which would capture the image of the retina of diabetic patients to classify the grade, severity, and types of DR; and the grade of DME without using dilating drops. It would also display, save, search and print the partial diagnosis that can be done to the patients. Diabetic patients, endocrinologists and ophthalmologists of one of the medical centers in City of San Pedro, Laguna, Philippines tested the system. Obtained results indicated that the classification of DR and DME, and its characteristics using the system were accurate and reliable, which could be an assistive device for endocrinologists and ophthalmologists.