JOURNAL ARTICLE

Convolution Neural Network for Diabetic Retinopathy Detection

Hari Vamsi Yadavalli

Year: 2020 Journal:   International Journal of Recent Technology and Engineering (IJRTE) Vol: 9 (1)Pages: 2436-2440

Abstract

Diabetes-Retinopathy (DR) condition detection based on machine learning and image processing techniques makes use of the diabetic portion from the set of input images. Textural feature analysis is adopted for feature extraction. CNN is used to classify the extracted features. The execution of the proposed technique is carried out in MATLAB, and the analysis is based on the accuracy, sensitivity, specificity. In the light of analytic outcomes, it can be said that the introduced method performs better than the existing technique in terms of all the mentioned parameters.

Keywords:
Computer science Convolution (computer science) MATLAB Artificial intelligence Feature extraction Diabetic retinopathy Convolutional neural network Pattern recognition (psychology) Feature (linguistics) Set (abstract data type) Artificial neural network Sensitivity (control systems) Image (mathematics) Computer vision Diabetes mellitus Medicine

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Citation History

Topics

Retinal Imaging and Analysis
Health Sciences →  Medicine →  Radiology, Nuclear Medicine and Imaging
Artificial Intelligence in Healthcare
Health Sciences →  Health Professions →  Health Information Management
Brain Tumor Detection and Classification
Life Sciences →  Neuroscience →  Neurology

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