Identifying brain stroke lesions from medical images is essential for timely diagnosis, treatment planning, and patient tracking. Deep learning-based methods have demonstrated significant opportunities for brain stroke detection in both Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). The location of the lesioned tissue can be obtained using automated stroke lesion segmentation, which can aid in clinical practice by assessing and evaluating the risks connected with different treatments. Hence, this review aims to discuss and clarify the present research work on sub-acute brain ischemia detection using CT and MR images. Multiple CT and MRI datasets utilized for the observation are gathered and included. Different experimental tools used for the validations are detailed. A chronological review related to the suggested models according to the contributions is provided. Various machine learning as well as deep learning mechanisms utilized for the validations are tabulated. Several advancements and complications associated with the existing research works are tabulated and included in this work. Multiple performance metrics utilized in the validations are tabulated. At last, various difficulties are presented in the classical technique that need to be tackled in the upcoming research and explained as the research gaps. Hence, the developed research work effectively helps the upcoming professionals gain more information related to their research.
Saranya et al. (Mon,) studied this question.
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