Disease Prediction Based On Retinal Images using Neural Network Classification
R. Bhavani1, V. Prakash2, S. Balakumar3
1R. Bhavani, Assistant Professor. Department of CSE, SASTRA Deemed to be University, Kumbakonam, Tamil Nadu, India.
2V. Prakash, Assistant Professor. Department of CSE, SASTRA Deemed to be University, Thanjavur, Tamil Nadu, India.
3S. Balakumar, III MCA Student, Department of CSE, SASTRA Deemed to be University, Kumbakonam, Tamil Nadu, India.
Manuscript received on 22 August 2019. | Revised Manuscript received on 05 September 2019. | Manuscript published on 30 September 2019. | PP: 3089-3095 | Volume-8 Issue-11, September 2019. | Retrieval Number: K24910981119/2019©BEIESP | DOI: 10.35940/ijitee.K2491.0981119
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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 eyes used to determine the health of someone. There are several maladies in human, like vascular diseases that leave telltale markings within the retina of human eyes. The image of the retina will be captured comparatively with a camera now each day with digital imaging technology there’s abundantly advanced within the technology of computer analysis of the retinal pictures were accustomed identify the consequences of diseases like cardiovascular diseases in the human body. A retinal image provides the data of what’s going to happen within the body of a human. Significantly, the retinal vessel shows the condition of the cardiovascular in the physical body. Retinal pictures will offer the data concerning pathological changes within the physical body caused due to the disease in the retina that reveals cardiovascular disease, disorder, diabetes, and stroke. Computer-aided analyzed the image of the retina for the diagnostic purpose of the malady. However, automation of retinal segmentation that is difficult as a result of that the retinal pictures are noisy, distinction low, and therefore the vessel breadth often varies from very large to very tiny. Therefore, during this project, we are able to implement automatic vessel segmentation approach supported the neural network strategies to offer info regarding blood vessel and vein within the human membrane. Finally, cardiovascular diseases and therefore the alternative diseases expected victimization the distinctive technique of comparison of CENTRAL RETINAL EQUIVALENT OF VEIN and CENTRAL RETINAL EQUIVALENT OF ARTERY measurements.
Keywords: Image processing, Eye components, Disease diagnosis, Cardio-vascular diseases, Classification, Support Vector machine.
Scope of the Article: Signal and Image Processing