Diagnosis of Peripheral Artery Disease using Cnn Classifier
Daya Florance D1, Ajitha E2, Shobhanjaly P3, Gopinath M4
1Daya Florance D, Assistant Professor, Department of Information Technology, Vel Tech High Tech Dr.Rangarajan Dr. Sakunthala Engineering College, Chennai.
2Ajitha, Assistant Professor, Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, Chennai,
3Shobhanjaly P, Assistant Professor, Department of Information Technology, Vel Tech High Tech Dr.Rangarajan Dr. Sakunthala Engineering College, Chennai.
4Gopinath M, Assistant Professor, Department of Information Technology, Vel Tech High Tech Dr.Rangarajan Dr. Sakunthala Engineering College, Chennai.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 5438-5443 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6681018520/2020©BEIESP | DOI: 10.35940/ijrte.E6681.018520
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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: Peripheral Arterial Disease is common to all elderly peoples, which reduces the blood flow to the limbs. Due to PAD, the affected person unable to walk and gives pain while they try to walk. This PAD does not have any specific symptoms to affected persons in the earlier stage. This paper presents a solution to find the disease in which stage the person was affected. The Peripheral arterial disease is evaluated using convolution neural network classifier to identify in early stage to take treatments. The affected persons image (particular part of the body. Eg. Leg) is compared with the dataset. The dataset contains the collection of images that contains both normal and Peripheral arterial disease affected images. The CNN classifier compares with the dataset and shows that the given input image is in normal stage or it is affected by the Peripheral Artery disease. The accuracy level is high. This methodology helps to find the disease in earlier stage.
Keywords: Peripheral Arterial Disease, CNN Classifier, Dataset, Accuracy.
Scope of the Article: Design and diagnosis