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A medical decision making system for brain tumor identification from magnetic resonance images using machine learning techniques

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dc.contributor.author Al-Sarraf, Zahraa Abd Al Rahman Mohammed
dc.date.accessioned 2023-09-07T11:32:08Z
dc.date.available 2023-09-07T11:32:08Z
dc.date.issued 2021
dc.identifier.uri http://dspace.yildiz.edu.tr/xmlui/handle/1/13476
dc.description Tez (Doktora) - Yıldız Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2021 en_US
dc.description.abstract Brain tumor is an abnormal and uncontrolled growth of the cells. Early brain tumor detection is essential to save lives. In fact, brain tumors are difficult to diagnose, requiring specialized equipment and training. A medical decision making system facilitates diagnostic process by visualizing the data produced by a classification system, allowing doctors to make a right diagnosis. This study proposes an automated system for segmentation and classification the brain tumor grades in MRI into three classes: normal, LGG and HGG. In the proposed system, a new segmentation method named LDI-Means algorithm (Local Difference in Intensity-Means algorithm) is used. It is a clustering technique based on the difference in the intensity level of one pixel than another. Furthermore, a new approach in selecting the sub-significant set of attributes is used, denoted MI+SVD (Mutual Information + Singular Value Decomposition). The robust features are later used as an input to the classifier. The new network structure called simplified RNN (Residual Neural Network) is also offered by this study. The proposed automated system has six stages; the pre-processing, clustering by LDI-Means, feature extraction, feature selection and dimension reduction by MI+SVD, and classification by simplified RNN. The experimental findings at the end of the segmentation stage presented an approximate match of 99.02% with the hand-labeled images. In addition, in comparison to the original feature space and two standard dimension reduction xiv methods, PCA and SVD, the MI+SVD algorithm offered a more efficient result for improving the classification process to achieve a satisfied grading of brain tumors. Furthermore, using a simplified RNN as a classifier provides a high level of effectiveness to the proposed system. In comparison with other published studies, it is found that the proposed system is very sufficient to offer a meaningful real-time estimation for identification the brain tumor grades. en_US
dc.language.iso en_US en_US
dc.subject Medical decision making system en_US
dc.subject Brain image classification en_US
dc.subject Brain tumor en_US
dc.subject Segmentation Clustering en_US
dc.subject Image processing en_US
dc.subject Machine learning Mutual information (MI) en_US
dc.subject principal component analysis (PCA) en_US
dc.subject Singular value decomposition (SVD) en_US
dc.subject Support vector machine (SVM) en_US
dc.title A medical decision making system for brain tumor identification from magnetic resonance images using machine learning techniques en_US
dc.type Thesis en_US


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