Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques
Article
Vidhya, V, Raghavendra, U., Gudigar, Anjan, Kasula, Praneet, Chakole, Yashas, Hegde, Ajay, Menon, Girish, Ooi, Chui Ping, Ciaccio, Edward J. and Acharya, U. Rajendra. 2022. "Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques." Informatics. 9 (1). https://doi.org/10.3390/informatics9010004
Article Title | Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques |
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ERA Journal ID | 210592 |
Article Category | Article |
Authors | Vidhya, V, Raghavendra, U., Gudigar, Anjan, Kasula, Praneet, Chakole, Yashas, Hegde, Ajay, Menon, Girish, Ooi, Chui Ping, Ciaccio, Edward J. and Acharya, U. Rajendra |
Journal Title | Informatics |
Journal Citation | 9 (1) |
Article Number | 4 |
Number of Pages | 14 |
Year | 2022 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2227-9709 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/informatics9010004 |
Web Address (URL) | https://www.mdpi.com/2227-9709/9/1/4 |
Abstract | Traumatic Brain Injury (TBI) is a devastating and life-threatening medical condition that can result in long-term physical and mental disabilities and even death. Early and accurate detection of Intracranial Hemorrhage (ICH) in TBI is crucial for analysis and treatment, as the condition can deteriorate significantly with time. Hence, a rapid, reliable, and cost-effective computer-aided approach that can initially capture the hematoma features is highly relevant for real-time clinical diagnostics. In this study, the Gray Level Occurrence Matrix (GLCM), the Gray Level Run Length Matrix (GLRLM), and Hu moments are used to generate the texture features. The best set of discriminating features are obtained using various meta-heuristic algorithms, and these optimal features are subjected to different classifiers. The synthetic samples are generated using ADASYN to compensate for the data imbalance. The proposed CAD system attained 95.74% accuracy, 96.93% sensitivity, and 94.67% specificity using statistical and GLRLM features along with KNN classifier. Thus, the developed automated system can enhance the accuracy of hematoma detection, aid clinicians in the fast interpretation of CT images, and streamline triage workflow. |
Keywords | CAD; meta-heuristic algorithms; traumatic brain injury (TBI); intracranial hematoma; computed tomography |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Byline Affiliations | Manipal Academy of Higher Education, India |
Institute of Neurological Sciences, United Kingdom | |
Singapore University of Social Sciences (SUSS), Singapore | |
Columbia University, United States | |
Ngee Ann Polytechnic, Singapore | |
Asia University, Taiwan |
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https://research.usq.edu.au/item/z1w44/automated-intracranial-hematoma-classification-in-traumatic-brain-injury-tbi-patients-using-meta-heuristic-optimization-techniques
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