Muhammad Naeem Akram1, Muhammad Usman Yaseen1, Muhammad Waqar1, Muhammad Imran1,*, Aftab Hussain2
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3727-3744, 2023, DOI:10.32604/cmc.2023.041855
Abstract This study presents a deep learning model for efficient intracranial hemorrhage (ICH) detection and subtype classification on non-contrast head computed tomography (CT) images. ICH refers to bleeding in the skull, leading to the most critical life-threatening health condition requiring rapid and accurate diagnosis. It is classified as intra-axial hemorrhage (intraventricular, intraparenchymal) and extra-axial hemorrhage (subdural, epidural, subarachnoid) based on the bleeding location inside the skull. Many computer-aided diagnoses (CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan levels. However, these approaches perform only binary classification and suffer from a large number of parameters, which… More >
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