CT Scans Reimagined: Early Intracranial Hemorrhage Detection with AI

Several research projects have been centred on computer-aided diagnosis techniques. They operate on the presumption that photographs of various human body components may be processed and assessed to provide an immediate and precise diagnosis. Brain haemorrhage is a kind of stroke. An artery in the brain rupture, causing nearby tissues to bleed selectively. Brain tissue is destroyed by this hemorrhage. Brain hemorrhage requires an immediate diagnosis and rapid treatment to improve patient outcomes. Clinicians must be able to recognize and categorise brain haemorrhage in order to successfully treat it. CT (Computed Tomography) scans are used to detect internal bleeding and fractures. Due to their better accessibility, reduced price, and sensitivity to early hemorrhages, CT scans have several advantages over Magnetic Resonance Imaging (MRI) images. Deep learning is being employed in this situation. In this study, a machine learning method is proposed by using medical imaging to identify brain hemorrhages.