AI-Powered Big Data Analytics Framework for Automated and Accurate Detection of Intracranial Hemorrhage in Computed Tomography Imaging Using Advanced Deep Learning and Medical Image Processing Techniques

Traumatic brain injury-related intracranial hemorrhage (ICH) is potentially fatal and needs to be diagnosed quickly. The main tool is computed tomography (CT) scans, but skilled radiologists must interpret them. Variability in hemorrhage appearance and limited radiologist availability can delay the diagnosis and treatment. In this manuscript, an AI-Powered Big Data Analytics Framework for Automated and Accurate Detection of ICH in CT Imaging with Advanced Deep Learning (DL) and Medical Image Processing Techniques (ADIH-CTI-GGNN) is proposed. The goal is to accurately segment intracranial hemorrhages from computerized tomography images using automated image analysis methods, improving rapid diagnosis and supporting clinical decision-making. The first step is to get the input CT images from the RSNA Intracranial Hemorrhage (ICH) Detection Dataset. The data is pre-processed using the Dual Adaptive Unscented Kalman Filter (Dual, AUKF) for image resizing and normalization. The pre, processed images are then segmented using a structured Doubly Stochastic Graph, based Clustering (SDSGC) to accurately localize hemorrhagic regions in ICH CT images. After that, the Spatial, Spectral Representation Transform (SSRT) is used to obtain the features that have discriminative power. These features are classified using Gegenbauer Graph Neural Networks (GGNN) to identify ICH subtypes, such as no-ICH, epidural, intraparenchymal, intraventricular, subarachnoid, and subdural hemorrhage. The Adaptive Tasmanian Devil Optimizer (ATDO) is applied to optimize GGNN weight parameters. The proposed ADIH-CTI-GGNN framework, implemented in Python, achieves superior performance with 99.9% accuracy, 98% recall, 98.5% precision, and 98% F1-score, outperforming existing methods including ICH Segmentation using CNN (IHS-CTI-CNN), Brain Hemorrhage Detection utilizing Machine Learning (BHD-ML), and YOLO models for ICH detection using varied CT data sources (IHD-CTD-YOLOv5).