An Effective Model for Detection of Dysfunctionality in Heart Based on Iridology using Deep Neural Networks
Sreenivasa Rao Palavalasa1, Pradeep Kumar Bheemavarapu2, Siri Sahasra Jain3, Sai Sree Piriya4, Pavan Kumar Tadiparthi5
1Sreenivasa Rao Palavalasa*, Associate Professor, Department of Information Technology M. V. G. R. College of Engineering, India.
2Pradeep Kumar Bheemavarapu, Student, Department of Information Technology M. V. G. R. College of Engineering, India.
3Siri Sahasra Jain, Student, Department of Information Technology M. V. G. R. College of Engineering, India.
4Sai Sree Piriya, Student, Department of Information Technology M. V. G. R. College of Engineering, India.
5Pavan Kumar Tadiparthi, Associate Professor, Department of Information Technology M. V. G. R. College of Engineering, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 27, 2020. | Manuscript published on March 10, 2020. | PP: 1877-1881 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2888039520 /2020©BEIESP | DOI: 10.35940/ijitee.E2888.039520
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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: In today’s world heart disease is the primary reason for deaths. WHO has anticipated that 12 million people die every year because of heart diseases. Every organ of the body is represented in the iris in a well-defined manner. The Iris is a micro-structure of the entire body. The abnormality of the heart can be detected using Iridology science. In this article, we examine the heart dysfunctionality through a chain of steps which are localization of iris, segmentation of iris, ROI extraction, histogram equalization of ROI and classification using deep convolutional neural network. The results are assessed based on various standards such as precision, recall, fscore & accuracy.
Keywords: Cardiovascular disease (CVD), Iridology, CNN (Convolutional Neural Network), VGG16 (Visual Geometry Group), Down Sampling, Localization, Segmentation, ROI (Region of Interest), Histogram Equalization, Classification.
Scope of the Article: Neural Information Processing