Convolutional Neural Network Analysis of Tissue Remodeling and Myopathy in Peripheral Arterial Disease

Abstract

One of the most widely spread vascular diseases worldwide and in the United States is Peripheral Arterial Disease The disease classification is based on clinical testing and the judgment of physicians. Our goal is to demonstrate the applicability of artificial neural networks as an objective diagnostic tool for medical use. Patients with Peripheral Arterial Disease have different levels of arterial damage, which results in a chronic lack of blood supply in the lower extremities. As a result, these patients develop structural changes in their tissues, with detrimental long-term effects. We are presenting the results obtained from the analysis of human muscle specimens, obtained from vascular patients, using several different convolution neural networks and transfer learning. We used the clinical classification standards to produce the labels for our dataset and we were able to successfully develop 11 different Artificial Neural Network Models for objective patient classification.