Intracranial Hemorrhage Detection Using Jensen–Shannon Guided Transformer with Adaptive Multi-Gradient Learning

Intracranial hemorrhage (ICH) is a life-threatening neurological condition that requires rapid and accurate diagnosis to reduce mortality and improve patient health. Computed tomography (CT) imaging is widely used for ICH detection. However, manual interpretation can be time-consuming and prone to errors, particularly in high-volume clinical settings. Recent studies have demonstrated the effectiveness of deep learning techniques in automating medical image analysis and improving diagnostic accuracy. In this study, we propose a novel deep learning model, MGiT-X, for the automated detection of intracranial hemorrhage using head CT images. The MGiT-X model is a hybrid deep learning architecture that uses dual scale Swin Transformer modules to extract features at multiple scales, capturing local and global contextual information on CT images. It has a Gradient Fusion mechanism to enhance feature representation by combining complementary features to distinguish between hemorrhagic and healthy tissue. In addition, to further improve feature representation, the use of Jensen–Shannon divergence is used to provide better mutual alignment and consistency between the distribution of features. An adaptive weight strategy is also employed to provide refinement to the importance of features for classification. MGiT-X is evaluated on two publicly available datasets including the Head CT Hemorrhage dataset and the Brain CT Hemorrhage dataset. The proposed approach leverages advanced feature extraction and classification capabilities to distinguish between hemorrhage and healthy cases effectively. Experimental results demonstrate that the proposed MGiT-X achieves high performance across both datasets. On Dataset 1, the model attains an overall accuracy of 95.87% and a Kappa score of 91.80%, while on Dataset 2, it achieves an improved accuracy of 99.12% with a Kappa score of 98.20%. Class-wise evaluation further shows strong performance, with F1-scores exceeding 95% for both hemorrhage and healthy classes across datasets.