Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques

V. Vidhya, U. Raghavendra, Anjan Gudigar, Praneet Kasula, Yashas Chakole, Ajay Hegde, R. Girish Menon, Chui Ping Ooi, Edward J. Ciaccio, U. Rajendra Acharya

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number4
JournalInformatics
Volume9
Issue number1
DOIs
Publication statusPublished - 03-2022

All Science Journal Classification (ASJC) codes

  • Communication
  • Human-Computer Interaction
  • Computer Networks and Communications

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