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CT-scan image of brain tumor.

CT-scan image of brain tumor.

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Brain tumor is serious and life-threatening because it found in a specific area inside the skull. Computed Tomography (CT scan) which be directed into intracranial hole products a complete image of the brain. That image is visually examined by the expert radiologist for diagnosis of brain tumor. This study provides a computer aided method for calcu...

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... All these imaging techniques are shown in Figure 1. Mohammed Kamil (Kamil 2015) presented his framework of a computer aided method that successfully detected the region of brain tumor from s Computed Tomography (CT) scan. The framework used the techniques of image enhancement and mathematical morphology, coupled with thresholding to segment the tumor. ...
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Brain tumor is a commonly known medical condition which has a very low survival rate due to delayed diagnosis and treatment. With the advancement of information technology and machine learning, various computer aided techniques have been studied in recent years to segment tumor region from given brain scan for early detection and treatment. However, health workers are still hesitant to adopt those techniques into their clinical systems due to insufficient comprehensive analysis of those techniques. Therefore, in this paper we compare the segmentation results of various computer aided algorithm. First, we present a survey of various segmentation techniques based on region-based, unsupervised learning and classifier approaches for tumor detection from MRI of the brain. These techniques differ based on using proximity and features of pixel for segmenting desired region. Next, we take a well-known and accepted algorithm for each of these categories to test the results. The selected algorithms are Marker Controlled Watershed, K-Means Clustering and Convolutional Neural Networks (CNN). 3D Brain MRI scans are used for the study where the data images are first pre-processed and converted into 155 2D slices for each image. Further, all these 155 2D slices are processed using three chosen algorithms. The results of the three techniques are then compared using evaluation metrices like DICE and IOU. The comparison results show that the classifier-based technique, i.e., CNN outperforms the others with a high accuracy of 83-86%. This research is expected to help medical workers to make an informed decision and implement computer aided systems to their clinical processes to reduce the manual labour and time.
... On the basis of one particular feature, each interior node that includes decision criteria is based. The entropy reduction that presents the purity of samples is used to calculate the features that are in relevance to classification [8]. The classifier through which two classes are separated using a hyper plane is known as Support Vector Machine (SVM). ...
... 978-1-5386-4844-5/18/$31.00 ©2018 IEEE In this regard, Mohammed Kamil in [12] presented a computer aided method to detect the brain tumor using Computed Tomography (CT) Scan images of brain. The methodology used included the use of threshold values, image enhancement and application of morphological operations. ...
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... In this regard, Mohammed Kamil in [12] presented a computer aided method to detect the brain tumor using Computed Tomography (CT) Scan images of brain. The methodology used included the use of threshold values, image enhancement and application of morphological operations. ...
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