@inproceedings{f7aaa83297944f65a35d4f3519b95b1e,
title = "Identification of Abnormalities in Head Computerized Tomography Scans",
abstract = "Stroke and traumatic brain injuries are both leading causes of death and long-term disability globally [1]. Early detection of abnormalities in head computerized tomography (CT) scans reduces patient risk of serious medical complications from brain injuries such as hemorrhagic stroke and cranial fractures [2]. A machine learning algorithm trained to autonomously identify and classify head CT scan irregularities has the potential to decrease detection time of such anomalies and allow for quicker, more effective treatment.",
author = "M. Delrocini and C. Angelini and G. Rasool",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2020 ; Conference date: 05-12-2020",
year = "2020",
month = dec,
day = "5",
doi = "10.1109/SPMB50085.2020.9353610",
language = "English (US)",
series = "2020 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2020 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2020 - Proceedings",
address = "United States",
}