Abstract
Stroke is a severe disease that is currently the country’s fourth greatest reason of death. Ischaemic stroke is a medical emergency that necessitates prompt triage, diagnosis, and treatment. A patient’s stroke lesion site must be identified as soon as feasible and properly to assess the risks and give more early and efficient treatment. It is essential to develop automated methods for identifying and segmenting stroke lesions. In this work, MRI is sliced into two-dimensional network groups and used each division set to train sessions and predict individually. Finally, before slicing and splitting to produce a 3D lesion result, two-dimensional slices results of all groups are arranged according to the position order in the MRI.
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