Optimization of Intracranial Hemorrhage Using CT Scan Images and Feature Extraction

Abstract

Intracranial bleeding is among the most severe forms of brain stroke. The neurologic effects and artery rupture cause bleeding in the brain and the tissue around it. Haemorrhage is classified based on where the bleeding occurs on the brain. This paper depicts the application of multiple machine-learning approaches to separate CT scan images into normal and pathological categories. Separate analysis is conducted on the functionality of the features extracted from the various texturing approaches, such as the Grey Level Co-occurrence Matrix (GLCM) and Grey Level Run-Length Matrix (GRLM). Particle Swarm Optimisation (PSO) and K-Nearest Neighbours are used to choose relevant characteristics that increase the classification accuracy for feature extraction. The findings demonstrate that these texture features have excellent discrimination accuracy