Object Detection Networks For Localization And Classification Of Intracranial Hemorrhages

Abstract

Intracranial hemorrhages represent life-threatening brain injuries. This paper presents twostate-of-the-art object detection systems (Faster R-CNN and YOLO v2) which are trained to localizeand classify hemorrhages in axial head CT slices by providing labelled rectangular bounding boxes.Publicly available datasets of head CT data and ground truth bounding boxes are used to evaluate andcompare the performance of both detectors. The Faster R-CNN shows better results by achieving anaverage Jaccard coefficient of 58.7 %.