Paper Info
Title
Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques
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.
Year
DOI
Venue
2022
10.3390/informatics9010004
INFORMATICS-BASEL
Keywords
DocType
Volume
traumatic brain injury (TBI), intracranial hematoma, computed tomography, CAD, meta-heuristic algorithms
Journal
9
Issue
ISSN
Citations 
1
2227-9709
0
PageRank 
References 
Authors
0.34
0
10
Name
Order
Citations
PageRank
V. Vidhya100.34
U. Raghavendra201.01
anjan gudigar3807.50
Praneet Kasula400.34
Yashas Chakole501.01
Ajay Hegde600.34
Girish R. Menon700.34
Chui Ping Ooi811.37
Edward J. Ciaccio916530.79
Rajendra Acharya U104666296.34