Weakly Semi-supervised Classification of Transcranial Doppler Ultrasound Signal for Ischemic Stroke Detection

Abstract

Stroke is the prominent cause of morbidity and mortality among people worldwide. This study focused to solve the problem of classifying the embolus and artefact from the segmented high intensity transient signal (HITS) by proposing weakly semi-supervised learning approach with active learning. Here, the active learning was executed to select the least confident data for expert labeling. This strategy really improves in detecting embolus from unrestricted topology area and at the same time minimizing the need of a large numbers of label data whilst keeping its performance. Data employed for this study was collected from in-vitro experimental setup. A total of 540 HITS has been used for evaluation. The experimental results proved the advantages of our proposed classification system which showing promising results compared to baseline systems by giving 82.52% and 93% of accuracy and sensitivity, respectively. The system was able to only use a small amount of training data which can reduce the cost of labeling. Furthermore, the proposed system can be adaptively trained using data from different arteries i.e. posterior cerebral artery (PCA), middle cerebral artery (MCA) and internal carotid artery (ICA).