Intracranial Haemorrhage Detection Based on Deep Learning Using CT Images

Abstract

Intracranial haemorrhage (ICH) is a medical condition that can have life-threatening consequences if it is not promptly diagnosed and treated. Diagnostic tools for medical imaging, such computed tomography (CT) scans, can help identify ICH. In this study, we investigated the application of deep learning methods to CT scan images used for ICH diagnosis. In particular, the authors made use of the Block Matching 3D (BM3D) algorithm for image pre-processing, the Densenet121 neural network design, and the Hough Transformation for image transformation. The objective was to create a model that could correctly identify and categorize ICH in CT scan pictures. The region of interest (ROI) in the CT scan pictures was then located and extracted using the Hough Transformation approach. Overall, the findings of this study show the potential of deep learning methods to support ICH diagnosis from CT scan pictures. The suggested approach was highly accurate and has the potential to help radiologists identify ICH.