Diagnosis of Peripheral Artery Disease using Cnn Classifier

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.