Background: The sub-acute ischemic stroke is the most basic illnesses reason for death
on the planet. We evaluate the impact of segmentation technique during the time of breaking
down the capacities of the cerebrum.
Objective: The main objective of this paper is to segment the ischemic stroke lesions in Magnetic
Resonance (MR) images in the presence of other pathologies like neurological disorder, encephalopathy,
brain damage, Multiple sclerosis (MS).
Methods: In this paper, we utilize a hybrid way to deal with segment the ischemic stroke from alternate
pathologies in magnetic resonance (MR) images utilizing Random Decision Forest (RDF)
and Gravitational Search Algorithm (GSA). The RDF approach is an effective machine learning
Results: The RDF strategy joins two parameters; they are; the number of trees in the forest and
the number of leaves per tree; it runs quickly and proficiently when dealing with vast data. The
GSA algorithm is utilized to optimize the RDF data for choosing the best number of trees and the
number of leaves per tree in the forest.
Conclusion: This paper provides a new hybrid GSA-RDF classifier technique to segment the
ischemic stroke lesions in MR images. The experimental results demonstrate that the proposed
technique has the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE),
and Mean Bias Error (MBE) ranges are 16.5485 %, 7.2654 %, and 2.4585 %individually. The
proposed RDF-GSA algorithm has better precision and execution when compared with the existing
ischemic stroke segmentation method.