Design a Novel Framework for Traumatic Brain Injury Classification Using Deep Learning Algorithm

Accurate and rapid classification is crucial for the treatment and prognosis of traumatic brain injuries (TBls), a major worldwide public health concern. Conventional techniques for TBI categorization often depend on subjective evaluations or restricted imaging methods, resulting in less-than-ideal results. A novel framework for Traumatic Brain Injury (TBI) classification using hybrid deep learning methods, namely MobileNetv2 and VGG16, is presented in this research. The suggested system is tested on a wide-ranging dataset including various TBI patients, showing better performance than conventional classification approaches. Our work introduces a promising method for classifying Traumatic Brain Injuries (TBI) by using advanced deep learning techniques. This approach has the potential to improve diagnosis, treatment planning, and patient outcomes.