Integrating convolutional and transformer networks for precise diagnosis of watershed and hemorrhagic stroke.

ABSTRACT: Ischemic watershed strokes (IWS), also known as border zone infarcts hold significant importance in neurology, however, are often overlooked type of Ischemic Stroke. They are difficult to detect due to their subtle appearance and small sizes. Current diagnostic methods are limited in their ability to effectively identify IWS which represents only 5% of all Ischemic stroke cases and that is why most existing approaches for stroke segmentation and classification focus on other mostly occurring stroke types. This leaves a gap in the effective management of the disease. Additionally, machine learning models often struggle to differentiate between small and large strokes, limiting their ability to generalize across different stroke sub-types. This study aims to introduce a new method for identi

SUBMITTER: Kousar T 

PROVIDER: S-EPMC12644620 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature