Use of Convolutional Neural Network for Detection of Intracranial Hemorrhage

Abstract

Intracranial hemorrhage is a medical disorder that occurs when a cranial blood vessel ruptures. Due to the complexity of the pathology, early detection is essential for effective treatment. Computed Axial Tomography (CAT) is essential for the treating physician to understand the location and severity of hemorrhage, the risk of impending cerebral injury, and to guide often emergent patient treatment; however, this paper develops an intelligent system to provide technological tools to support the diagnosis and detection of intracranial hemorrhages by implementing convolutional neural networks. This paper aims to obtain a neuroimaging dataset, perform feature detection through image analysis, and binarily classify the disease. Using this intelligent system as a support tool for the detection of intracranial hemorrhages will contribute significantly to improving diagnosis time and timely and reliable treatment of this disease.