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.
Highlights
Traumatic Brain Injury (TBI) is a neurological disorder with high rates of disability and mortality worldwide
Each image is subdivided into blocks of 128 × 28, and the features are obtained from each sub block using the various feature extraction schemes described earlier
The proposed technique achieved an optimum performance of 95.74% accuracy, a sensitivity of 96.93%, and a specificity of 94.67% using a combination of Gray Level Run Length Matrix (GLRLM) and statistical features along with the Grey Wolf Optimization technique
Summary
Traumatic Brain Injury (TBI) is a neurological disorder with high rates of disability and mortality worldwide. TBI includes both primary and secondary injuries, which can progressively deteriorate brain function. Most TBI survivors suffer from physical and mental disabilities that require long-term support and medical attention [1,2,3]. TBI can cause accumulation of blood (hemorrhage) inside the cranium leading to increased intracranial pressure. The global annual frequency of TBI occurrence and mortality are predicted to be 369 and 20, respectively, among 100,000 subjects. 5–10% of mortality is due to injuries, and 40% of the mortality can be attributed to TBI. There was an 8.4% increase in the age-standardized prevalence of TBI between 1990 and 2016 [4]. Early identification and diagnosis of hemorrhage is crucial for TBI severity detection and patient management
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