This abstract outline a groundbreaking approach employing deep learning techniques for automated analysis of cerebrovascular images, crucial for diagnosing strokes and aneurysms promptly and accurately. Leveraging convolutional neural networks (CNNs) and recurrent neural networks (RNNs) on diverse imaging modalities like MRI, CT scans, and angiography, this study focuses on feature extraction, segmentation, and temporal analysis of vasculature changes. Trained on a vast annotated dataset, the model’s evaluation metrics encompass accuracy, sensitivity, and specificity for detecting abnormalities, classifying lesions, and predicting risks. Additionally, the research delves into visualizing the model’s decisions, enhancing interpretability and offering insights into critical diagnostic factors. This deep learning-based approach shows promise in revolutionizing cerebrovascular disease diagnosis, potentially enabling rapid, accurate, and reliable assistance for healthcare professionals and ultimately improving patient outcomes through timely interventions.

Deep learning, machine learning, classification, segmentation, brain tumor, MRI.