New classification models for the diagnosis of coronary artery disease from spect myocardial perfusion imaging

The main goal of this dissertation is to develop computer-aided classification models to identify the presence and localization of coronary artery diseases, namely ischemia and infarction, from single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). Two different classification models, namely deep learning (DL)-based and knowledge-based, are proposed. The first model utilizes a deep neural network (DNN) trained from scratch, transfer learning with pre-trained DNNs, various classifiers as support vector machine, boosted decision tree, random forest, with deep and shallow features extracted from those networks. The latter model, on the other hand, aims to transform the knowledge of expert readers to appropriate image processing techniques including particular color thresholding, segmentation, feature extraction, and some heuristics. Besides, the images from 192 patients, who were referred for 1-day rest/stress Tc-99m SPECT MPI were collected to constitute a publicly available dataset. Visual assessment of two expert readers on this dataset is used as a reference standard. The performances of the proposed models were then evaluated according to this standard. The maximum accuracy, sensitivity, and specificity values are computed as 94%, 88%, 100% for the DL-based model, 93%, 100%, 86% for the knowledge-based model, respectively. The proposed models provide diagnostic performance close to the level of expert analysis. Therefore, they can aid in clinical decision making for the interpretation of SPECT MPI regarding myocardial ischemia and infarction.