Publication

Abstract : Etiology of ischemic stroke can be attributed to large vessel occlusions (LVOs), which lead to insufficient supply of oxygen to brain. Early detection and evaluation of infarct core volume plays a crucial role in the optimal treatment for brain ischemia. In this work, we leverage transfer learning for automated computing of Alberta Stroke Program Early CT Score (ASPECTS) using Non-Contrast CT (NCCT) scans. We compare the performance of different state-of-the art ImageNet pre-trained networks for multi-class classification of NCCT scans based on ASPECTS score value ranging from 0-10. Additionally, based on ASPECTS and reperfusion therapy, we group the NCCT scans into two categories: 0-6 in Group0 and 7-10 in Group1, to perform binary classification using pre-trained networks. Our experiments validate the choice of pre-trained EfficientNetV2 with the highest Area Under Curve (AUC) of 0.995 in multi-class classification and 0.977 in binary class classification.