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Paper Title: Automatic Brain Tumor Detection using SVM and FBB Algorithm
Authors Name: Miss. Rajeshwari G. Tayade , Mr. C.S. Patil , Mr. R. R. Karhe
Unique Id: IJSDR1607053
Published In: Volume 1 Issue 7, July-2016
Abstract: This Paper represents an algorithm for detection of brain tumor in MR images. Brain tumor is an uncontrolled growth of intracranial cells. The early detection of cancer can be helpful in complete curing the disease. According to the most research in developed countries shows results that just because of inaccurate detection the numbers of people who have brain tumor were died. As the use of digital images has rapidly increased over the past decade, Radiologists by using computed Tomography (CT scan) and Magnetic Resonance Imaging (MRI) examine the patient physically. In surgical & medical assessments, brain tumor segmentation using MRI images is very difficult and important task. For diagnosis of brain tumor MR image is visually examined by the physician. However this method of manual detection resists accurate tumor detection and more time consuming. To overcome these problems, this paper uses computer aided techniques such as SVM for extraction of tumor is key component to automate specific radiological tasks for the characterization of anatomical structures and regions of interest and FBB algorithm to locate tumor area on the MRI images [1]. At the end of process the tumor detected from the MR image and its exact position and the shape also determined. This technique allows the segmentation of brain tumor tissue with accuracy, improved performance and robustness; it also reduces the effect of noise.
Keywords: Fast bounding box, Magnetic Resonance Imaging, Support vector machine.
Cite Article: "Automatic Brain Tumor Detection using SVM and FBB Algorithm", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.1, Issue 7, page no.318 - 323, July-2016, Available :http://www.ijsdr.org/papers/IJSDR1607053.pdf
Downloads: 0001411
Publication Details: Published Paper ID: IJSDR1607053
Registration ID:160649
Published In: Volume 1 Issue 7, July-2016
DOI (Digital Object Identifier):
Page No: 318 - 323
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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