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Title: Detection of Brain Tumor using Machine Learning Approach
Authors: Chadha, Megha
Supervisor: Jain, Sushma
Keywords: SVM;CNN;Machine Learning;Segmentation
Issue Date: 2-Sep-2019
Abstract: Tumor in brain is one of the most dangerous diseases which if not detected at the early stage using accurate methods can even risk the life. Currently, the methods used by neurologists for analysis are not completely error free and states that manual Tumor is basically divided into two types: Benign tumor and malignant tumor. The study discussed both the types of tumor along with their symptoms and the ways tumor can be treated. The purpose of the thesis is to detect the tumor, segment it from the MR image of the brain and classify it into either benign tumor or malignant tumor. The study presents machine based approach for segmentation of brain images using thresholding segmentation technique followed by the identification and classification of tumor into its types using CNN classification approach. Comparison between machine learning approaches is shown to compare the results in terms of accuracy and classification output. Results are compared to find a better machine learning approach which improves the performance; minimize the complexity and works on real time data.
Description: ME Thesis
Appears in Collections:Masters Theses@CSED

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