A Deep Learning Approach with Stack of Sub-classifiers for Multi-label Classification of Obstructive Disease from Myocardial Perfusion SPECT

Artificial intelligence applications, especially deep learning in medical imaging, have gained much attention in recent years. With the computer's aid, Coronary artery disease (CAD) - one of the most dangerous cardiovascular diseases - is diagnosed effectively without human interference and efforts. A lot of research involving predicting CAD from Myocardial Perfusion SPECT has been conducted and given impressive results. However, all existing methods detect whether there is a disease or not. They do not provide information about which obstructive areas are (mainly in the left anterior descending artery (LAD), left circumflex artery (LCx), and right coronary artery (RCA) territories) that result in CAD. To further diagnose CAD, we develop new classifiers to solve a multi-label classification problem with the highest accuracy and area under the receiver operating characteristics curve (AUC) when compared to different methods. Our proposed method is based on transfer learning to extract features from Myocardial Perfusion SPECT Polar Maps and a novel stack of sub-classifiers to detect particularly obstructive areas. We evaluated our methods with eight hundred and one obstructive images from a database of patients referred to a hospital from 2017 to 2019.

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Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).