Comparison of Multilevel Thresholding and Semantic Segmentation SegNet Methods for Stroke Detection Based on CT Scan Images of The Brain

Rochman, Arif Nur (2022) Comparison of Multilevel Thresholding and Semantic Segmentation SegNet Methods for Stroke Detection Based on CT Scan Images of The Brain. Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Advanced computer vision techniques such as deep learning show tremendous potential for extracting clinically significant information from medical images. The main common characteristic of deep learning methods is their focus on feature learning and has recently been applied to CT imaging for acute stroke. The presence of acute hemorrhagic stroke was confirmed clinically using non-contrast computed tomography (CT) imaging. The main objective of this study is to compare the multilevel thresholding and semantic segmentation segnet methods to detect stroke from a CT scan with accurate results. The research stages of the input data will detect the lesion area, which is then segmented and classified. The input is a CT scan image using the multilevel thresholding method and image segmentation to get the results of the classification of stroke types. This study obtained an average accuracy of SVM testing of 94.48% with a precision value of 0.7760 and a dice similarity of 0.6410 for the segnet method, while for the thresholding method, a precision value of 0.8890 and a dice similarity of 0.8379 was obtained. The conclusion of this study shows that the thresholding method is better than image segmentation. Keyword: Stroke, Multi Thresholding, CT Scan, Semantic Segmentation

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