Automated Intracranial Hemorrhage Detection Using Deep Learning in Medical Image Analysis

Abstract

The research work introduces a novel approach to automatically detect intracranial hemorrhage (ICH) using advanced deep learning algorithms. The method being examined efficiently extracts and localizes information from computed tomography (CT) brain data by utilizing attention processes that are connected to convolutional neural networks (CNNs). The results achieved through training and evaluating the model using a dataset of CT images was highly impressive. The model showed accuracy value of 99.76% and has an AUC-score of 0.9976. Through the incorporation of attention strategies into the CNN framework, the interpretability of the model can be enhanced, allowing for the identification of the most relevant regions for ICH detection. The findings of the paper highlight the potential of AI-driven medical diagnostics to enhance the speed and accuracy of neuroimaging analysis, leading to better patient outcomes. Significant advancements have been achieved in the automated diagnosis of cerebral hemorrhage through the utilization of deep learning algorithms with the proposed method.