Brain tumor detection and classification using image processing techniques
AuthorFayyadh, Sultan Bahr
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CitationFayyadh, S. B. (2021). Brain tumor detection and classification using image processing techniques, (Yayınlanmış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.
[Brain Tumor is the abnormal and uncontrolled growth of tissues or cells in the brain, the brain tumor is dangerous and life-threatening disease, Detection of the diseases through image processing is done by using an integrated approach working methods of processing MRI images of brain tumor entering it and distinguishes this approach if the brain is normal or abnormal, computer systems have been used in this area to analyze medical information, analyzing and extracting the most important features of the brain tumor and focusing on image analysis and processing techniques to distinguish between different diseases based on the symptoms of each disease. This work adopts two proposed approaches for detecting brain tumor using image processing and deep learning techniques with makes a comparison between these two approaches. This work was planned to some an important and common group of brain tumor, including Glioma, Meningioma, Pituitary Adenoma, and Nerve Sheath. These kinds of brain tumors are the most popular in the world. The dataset contains 3000 images related to malignant (normal) and benign (abnormal) each one has 1500 image. In the first proposed approach, where several steps are used in the form of stages, which are include, the image acquisition stage, image pre-processing, image segmentation, image post-processing, extraction the features, and the classification stage. Class support vector machine (SVM) algorithm was used to perform the classification process in the second proposed approach, the convolution neural network (CNN) was used through which the brain tumor are classified according to a special structure of this algorithm consisting of several layers. In these two proposed approaches, the tumor were classified to deferent classes was detected. The obtained results from the comparison between the two proposed approaches in terms of performance and accuracy showed the preference of the second approach which adopted the deep learning and using the CNN algorithm, over the first approach, because the overall accuracy rate that obtained from the second proposed approach was (98,29%). While the overall accuracy rate that obtained from the first proposed approach was (68.9%). So, the second proposed approach is more accurate and powerful in the process of detecting and classifying brain tumor.]
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