Automated Heart Dysfunctionality Identification Based on Iris using Deep Learning
Pavan Kumar Tadiparthi1, Pradeep Kumar Bheemavarapu2
1Pavan Kumar Tadiparthi*, Associate Professor, Department of Information Technology, MVGR College of Engineering, India.
2Pradeep Kumar Bheemavarapu, Student, Department of Information Technology, MVGR College of Engineering, India
Manuscript received on February 10, 2020. | Revised Manuscript received on February 26, 2020. | Manuscript published on March 10, 2020. | PP: 528-531 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2526039520/2020©BEIESP | DOI: 10.35940/ijitee.E2526.039520
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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: One of the most deadly diseases in the world is Heart Disease. The dysfunctionality of the heart at the early stage can be detected using iridology. The study of iridology describes the structure of the human iris as an observation of the condition of organs in the body. In this article, we explore the heart condition through a series of stages such as iris localization, segmentation, extraction of region of interest, histogram equalization and classification using convolutional neural network. The results are evaluated using various quality metrics such as precision, recall, f-score & accuracy.
Keywords: Iridology, Convolutional Neural Network, Segmentation, ROI (Region of Interest), Histogram Equalization, Down-sampling, Classification.
Scope of the Article: Deep Learning