| dc.description.abstract |
Strokes are a leading cause of premature mortality in developed and developing countries,
and early treatment assistance can significantly prolong a patient’s life. How quickly the
lesion is determined from MRI images is the primary rehabilitative step in stroke therapy.
However, manual lesion identification takes time and is susceptible to both intra- and inter
observer inconsistencies. Since manual lesion detection is highly time-consuming, it can
negatively impact patient outcomes and overall experience. To aid in diagnosis, treatment
planning, and analysis, medical image segmentation separates a medical image into indi
vidual parts or segments, each corresponding to a particular anatomical structure or tissue,
such as organs, lesions, or other areas of interest. In light of this, computerized estimation
of the outcome of the ischemic stroke lesion can assist physicians in better evaluating the
stroke and providing information on tissue outcomes. So, this will be an essential tool for
assessing the extent of brain cell damage. Convolutional neural networks (CNN) have been
extensively utilized in the categorization of abnormalities from brain images in recent years.
So, this can be achieved by accurately classifying the characteristics of ischemic stroke le
sions employing a convolutional neural network with convolutional layers. Consequently,
to separate the Ischemia in the current study, a deep-learning network is employed in this
thesis. The primary discovery of this work is the extraction of ischemic lesion character
istics by utilizing the InceptionV3 network and the preservation of Z-axis information by
employing a conventional 3D U-NET architecture. The proposed model was trained and
tested using the ISLES-2017 dataset, and the experimental results obtained an overall seg
mentation Dice Coefficient of 0.43. The results of this investigation show that the proposed
technique is superior to earlier studies. |
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