Deep Learning Fusion for Intracranial Hemorrhage Classification in Brain CT Imaging

Abstract

Brain hemorrhages are characterized by the rupture in the arteries of brain due blood clotting or high blood pressure (BP), presents a significant risk of traumatic injury or even death. This bleeding results in the damage in brain cells, with common causes including brain tumors, aneurysm, blood vessel abnormalities, amyloid angiopathy, trauma, high BP, and bleeding disorders. When a hemorrhage happens, oxygen can no longer reach the brain tissues and brain cells begin to die if they are depleted of oxygen and nutrients for longer than three or four minutes. The affected nerve cells and the related functions they control are damaged as well. Early detection of brain hemorrhages is crucial. In this paper an efficient hybrid deep learning (DL) model is proposed for the intracranial hemorrhage detection (ICH) from brain CT images. The proposed method integrates DenseNet 121 and Long Short-Term Memory (LSTM) models for the accurate classification of ICH. The DenseNet 121 model act as the feature extraction model. The experimental results demonstrated that the model attained 97.50% accuracy, 97.00% precision, 95.99% recall and 96.33% F1 score, demonstrating its effectiveness in accurately identifying and classifying ICH.