Convolution Neural Network for Diabetic Retinopathy Detection
Hari Vamsi Yadavalli
Hari Vamsi Yadavalli, Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 2436-2440 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2621059120/2020©BEIESP | DOI: 10.35940/ijrte.A2621.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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: CNN, Retinopathy, CNN, Sensitivity, Specificity, Accuracy.
Scope of the Article: Convolution Neural Network