Lesion Detection and Classification techniques for Diabetic Retinopathy
Jyoti P. Sawant1, Sachin A. Naik2, Santosh S. Chowhan3
1Jyoti P. Sawant*, Research Scholar, Faculty of Computer Studies, Symbiosis International University (Deemed University), Pune, Maharashtra, India.
2Sachin A. Naik, Asst. Professor, Symbiosis Institute of Computer Studies and Research, Symbiosis International University (Deemed University), Pune, Maharashtra, India.
3Santosh S. Chowhan, Asst. Professor, Symbiosis Institute of Computer Studies and Research, Symbiosis International University (Deemed University), Pune, Maharashtra, India.
Manuscript received on January 23, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 3436-3441 | Volume-9 Issue-3, February 2020. | Retrieval Number: C6071029320/2020©BEIESP | DOI: 10.35940/ijeat.C6071.029320
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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 is a worldwide spread disease which is increasing rapidly and found in all age people. Diabetic Retinopathy is a retinal abnormality caused by diabetes. Which can lead to permanent vision loss or blindness. As Diabetic Retinopathy pathology damages retina without any early symptoms, it is very important to do the regular screening of retina and detection of Retinopathy. Ophthalmologist does the identification of Retinopathy manually which is time consuming and error prone. Hence, there is a need for early and correct automatic detection of Diabetic Retinopathy. Many researches have done for detection using Image Processing, Artificial Intelligence, Neural Network and Machine Learning. This paper presents a review on Diabetic Retinopathy Detection systems. This review highlights the public datasets available for the evaluation of the detection systems with different segmentation and classification techniques. We have discussed the analysis of different classification and segmentation techniques used in DR detection.
Keywords: Classification, Diabetic Retinopathy, Diabetic Retinopathy dataset, DR Lesions, DR Lesion Segmentation, Feature Extraction.