<p>Acute cerebral ischemic stroke lesions are regions of brain tissue damage brought on by an abrupt cutoff of blood flow, which causes oxygen deprivation and consequent cell death. The majority of strokes are ischemic strokes, which happen when a blood clot obstructs or narrows an artery that supplies blood to the brain. Depending on the location and extent of the afflicted area, these lesions can have serious implications, including neurological abnormalities like weakness, numbness, difficulty speaking, and paralysis. For prompt medical intervention and treatment, acute cerebral ischemic stroke lesions must be accurately identified. Because diffusion-weighted magnetic resonance imaging (DW-MRI) has a high sensitivity for identifying early tissue changes linked to ischemia, it is a commonly utilized imaging modality for evaluating ischemic stroke lesions. To enable prompt diagnosis and treatment planning, automated techniques for identifying and classifying acute cerebral ischemic stroke lesions from DW-MRI images are crucial. To evaluate MRI pictures and locate areas of aberrant tissue, these approaches usually combine deep learning architectures, machine learning algorithms, and sophisticated image processing techniques. These techniques can help medical professionals accurately assess the degree and severity of brain injury, guide treatment decisions, and forecast patient outcomes by automatically recognizing and segmenting ischemic stroke lesions. Better clinical results and a higher quality of life for stroke survivors can result from the early and accurate diagnosis of acute cerebral ischemic stroke lesions. Diffusion-weighted magnetic resonance imaging (DW-MRI) scans are essential for the diagnosis of acute cerebral ischemic stroke (ACIS), which is necessary for prompt and efficient therapy. This work suggests an innovative method for the automated diagnosis of ACIS lesions utilizing capsule graph neural networks, or capsule GNNs. Capsule GNNs use the graph-based representation of neural networks and the hierarchical structure of capsule networks to capture complex spatial relationships found in brain imaging data. These techniques can help medical professionals accurately assess the degree and severity of brain injury, guide treatment decisions, and forecast patient outcomes by automatically recognizing and segmenting ischemic stroke lesions. The approach entails preprocessing DW-MRI data to create brain connection graphs and extract pertinent information. These diagrams illustrate the anatomical and functional connections between several brain regions, offering important new perspectives on the intricate nature of ACIS lesions. These graph representations are then used to train the Capsule GNN architecture, which teaches it distinguishing patterns suggestive of ischemic stroke. The efficiency of the suggested method is illustrated by experimental findings on a dataset of DW-MRI scans from patients with ACIS. The Capsule GNN outperforms alternative deep learning architectures and conventional techniques in the detection and classification of ACIS lesions, achieving excellent accuracy in the process. Furthermore, physicians can have a better understanding of the underlying characteristics that contribute to the diagnosis thanks to the interpretable nature of Capsule GNNs, which boosts their trust in the automated assessment. Research shows how well Capsule GNNs can diagnose acute cerebral ischemia stroke from DW-MRI data. Key performance parameters, such as Dice Similarity Coefficient (DSC) of 84.13%, Sensitivity (SE) of 86.54%, Volume Difference (∆V) of 20.45%, Precision of 95.45%, Recall of 90.54, and F1-score of 96%. Through the utilization of both the graph-based neural network representation and the hierarchical structure of capsule networks, a method provides a potential foundation for precise and comprehensible medical imaging analysis in stroke care.</p>