Applications of deep learning algorithms in ischemic stroke detection, segmentation, and classification

Abstract

Ischemic, one of the fatal diseases characterized by insufficient blood supply to tissues poses a significant global health burden, necessitating the development of robust diagnostic and classification methodologies. Timely identification, intervention, and treatment are essential to reduce associated risk factors. Modern machine learning methods like deep learning and neural networks are being successfully employed on medical images to detect and segment the region of interest for various diseases where the performance of these computational methods is improving daily and for various tasks has surpassed natural intelligence. This success has convinced medical practitioners to trust computational methods and incorporate computer-based solutions into their clinical practices. It is, therefore, essential to examine the available solutions critically by considering their strengths and weaknesses to establish their trust and clinical applicability. In the context of the above-mentioned task, this work focuses on two aspects: first, a broad review has been done for Ischemic stroke prognostication using various brain-imaging biomarkers via diverse deep learning frameworks, and second, the reviewed works are categorized based on their computational approach employed for Ischemic stroke detection, segmentation, and classification. Finally, this work presents recent advances and future research directions to invent high-performance methods. It was concluded that recent advancements in ischemic stroke detection have achieved 85–98% accuracy using CNNs and transformer-based models with separate imaging, clinical, and molecular data, though combined analysis remains largely underexplored. Integrating vascular imaging, clinical signs, and proteomic data can enhance real-time monitoring. However, challenges persist in unifying diverse parameters, necessitating advanced methodologies such as transfer learning, multi-task learning, advanced transformers, federated learning, and standardized protocols. These findings pave the way for improved diagnostics, treatment, and outcomes in stroke management.