Intracranial Hemorrhage Detection using Deep Learning and Optimization Techniques

Intracranial Hemorrhage (ICH) is a critical medical condition characterized by bleeding within the skull, specifically in the brain. Timely detection and accurate classification of ICH from computed tomography (CT) scan pictures are essential for successful treatment plan and improved patient outcomes. This study presents a novel approach for ICH detection using Deep Learning and Machine Learning techniques. A hybrid model is proposed, leveraging the power of ResN et50, a state-of-the-art deep learning architecture, together with different machine learning methods. The hybrid models include ResN et50 with Support Vector Machines(SVM), Artificial Neural Networks(ANN), ANN optimized using the Grasshopper Optimization Algorithm, and ANN optimized using the Genetic Optimization Algorithm. By harnessing the learned features of ResN et50 and the optimization capabilities of these algorithms, the models aim to accurately identify and classify hemorrhage patterns in CT scans. Experimental evaluations on a dataset of CT scan images demonstrate the superiority and efficacy of the proposed hybrid models in achieving high classification accuracy. The results highlight the potential of this approach for aiding clinicians in early diagnosis and treatment planning of ICH, thus contributing to improved patient care in cases of intracranial hemorrhage.