Machine Learning Technique for Feature Extraction and Segmentation of Retinal Blood Vessels
Shivani Patil1, Pradnya Kulkarni2
1Shivani A Patil MTECH, School of Computer Engineering & Technology, MIT World Peace University, Pune.
2Prof. Dr. Pradnya Kulkarni, School of Computer Engineering & Technology, MIT World Peace University, Pune.
Manuscript received on April 02, 2020. | Revised Manuscript received on April 21, 2020. | Manuscript published on May 30, 2020. | PP: 952-955 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2223059120/2020©BEIESP | DOI: 10.35940/ijrte.A2223.059120
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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: The term Diabetic retinopathy is the serious issue that is caused by the diabetes, which affects the eyes that may lead to blindness. DR takes place due to damaged arteries and veins that are a part of the fundus of the eye. Although DR can be prevalent now days, its prevention remains challenging. Visual analysis of the funds and consideration of colour photographs Ophthalmologists directly examine the existence and severity of DR. This process is expensive as well as time consuming as there are huge number of diabetes affected people worldwide. The automatic Diabetic Retinopathy system is expanded to predict various related diseases that are analysed. The proposed methodology uses EYEPACS dataset that consists on 35,126 fundus images. These images are pre-processed and sent to neural network that detects type of DR. CNN detects clusters of pixels that are damaged in the macula region and in turn evaluates the overall damaged area in the macula from the retinal images. The retinal fundus images present structural and impulsive noise.
Keywords: Diabetic retinopathy, Fundus Imaging, Abnormal features, Machine Learning.
Scope of the Article: Machine Learning.