Automated Recognition of Diabetic Retinopathy using Machine Learning Techniques
J. Pradeep Kandhasamy1, S. Balamurali2, M. Arun3, S. Gokul Nath4
1J. Pradeep Kandhasamy, Department of Computer Applications Computing, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
2S. Balamurali, Department of Computer Applications Computing, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
3M. Arun, Department of Computer Applications Computing, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
4S. Gokul Nath, Department of Computer Applications Computing, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
Manuscript received on 01 December 2019 | Revised Manuscript received on 19 December 2019 | Manuscript Published on 31 December 2019 | PP: 602-606 | Volume-8 Issue-4S2 December 2019 | Retrieval Number: D11211284S219/2019©BEIESP | DOI: 10.35940/ijrte.D1121.1284S219
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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: Diabetic Retinopathy (DR) is the illness due to severe polygenic disorders that result in loss of vision for the patients. The development in computer science leads to the timely recognition of DR through an automatic system that is more advantageous than the diagnosis done by a doctor. This paper reviews the DR diagnosis technique that includes deep learning, machine learning and image processing based approaches and their performance. Among the machine learning approaches, the Artificial Neural Network (ANN) classification technique results in high accuracy. The green channel extraction based image contrast enhancement has high classification accuracy, which outperforms the image processing techniques. The performance of the model is estimated by the metrics including sensitivity, specificity and accuracy. This study presents depth insights of techniques for automated DR diagnosis.
Keywords: Artificial Neural Network, Artificial Intelligence, Diabetic Retinopathy, Image Processing, Machine Learning.
Scope of the Article: Machine Learning