A CNN Based Method for Brain Tumor DetectionPublic Deposited
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The thesis presents a new method of brain tumor detection and localization by using image segmentation and convolution neural network. In order to ensure the quality of the medical images, there are several image preprocessing techniques applied, which include the procedure of removing the noise and non-brain tissue and enhancing the contrast. By using active contour for segmentation, the tumor area is separated from the image as its energy appears different in pixels and the feature extraction reveals the mathematical properties of the tumor.After the tumor localization, the target regions are imported into to the CNN and CNN classifies them into categories based on the training results from the learning procedure. This thesis uses the 4-fold cross validation for result testing. With over 80% accuracy, the CNN shows great potential in tumor detection. In addition, this thesis covers the section of how parameter settings influencing the CNN performance.
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- Copyright © 2018 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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