Introduction. Artificial intelligence (AI) models have been employed to automate decision‐
making, from commerce to more critical fields directly affecting human lives, including …

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification
of lesion burden and accurate image processing. Current automated lesion segmentation …

Automated demarcation of stoke lesions from monospectral magnetic resonance imaging
scans is extremely useful for diverse research and clinical applications, including lesion …

Lesion studies are crucial in establishing brain-behavior relationships, and accurately
segmenting the lesion represents the first step in achieving this. Manual lesion segmentation …

Magnetic resonance imaging (MRI) is a standard tool for the diagnosis of stroke, but its
manual interpretation by experts is arduous and time-consuming. Thus, there is a need for …

Ischemic and hemorrhagic strokes are two major types of internal brain injury. 3D brain MRI
is suggested by neurologists to examine the brain. Manual examination of brain MRI is very …

This research proposes augmenting cropped computed tomography (CT) slices with data
attributes to enhance the performance of a deep-learning-based automatic left-femur …

Brain stroke has become a significant burden on global health and thus we need remedies
and prevention strategies to overcome this challenge. For this, the immediate identification …

Studies on predicting heart attacks using Machine Learning demonstrate that there is a wide
variety of algorithms and methodologies highlighting their impact on heart attack prediction …

Classification and subtyping of ischemic stroke play a significant role in therapeutic decision-
making, which leads to higher requirements on the infarct lesion location. This study …