Object Detection Networks For Localization And Classification Of Intracranial Hemorrhages

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116_EEICT_2021_2.pdf(915.65 KB)
Date
2021
Authors
Nemcek, Jakub
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Mark
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Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
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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 %.
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Keywords
intracranial hemorrhage, convolutional neural network, localization, classification, computedtomography, Faster R-CNN, YOLO v2
Citation
Proceedings II of the 27st Conference STUDENT EEICT 2021: Selected Papers. s. 116-120. ISBN 978-80-214-5943-4
https://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazeni
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Peer-reviewed
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en
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© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
DOI
10.13164/eeict.2021.116
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http://hdl.handle.net/11012/200824
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Student EEICT 2021: Selected Papers
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