Design and implementation of a computer-aided diagnosis system for brain tumor classification

Abd-Ellah, Mahmoud Khaled; Awad, Ali Ismail; Khalaf, Ashraf A. M.; Hamed, Hesham F. A.

doi:10.1109/icm.2016.7847911

Cited by 63 publications

(32 citation statements)

References 17 publications

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“…Khaled Abd-Ellah,M. et al [3] segmented MR images using K-means clustering then classified normal and abnormal tumors using SVM with features extracted via wavelet transform as input. Lang, L. et al [4] used traditional convolutional neural networks (CNNs) for brain tumor segmentation.…”

Section: Literature Surveymentioning

confidence: 99%

Malignant and Benign Brain Tumor Segmentation and Classification Using SVM with Weighted Kernel Width

Rezaei¹,

Agahi²

2017

SIPIJ

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In this article a method is proposed for segmentation and classification of benign and malignant tumor slices in brain Computed Tomography (CT) images. In this study image noises are removed using median and wiener filter and brain tumors are segmented using Support Vector Machine (SVM). Then a two-level discrete wavelet decomposition of tumor image is performed and the approximation at the second level is

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Section: Literature Surveymentioning

confidence: 99%

Malignant and Benign Brain Tumor Segmentation and Classification Using SVM with Weighted Kernel Width

Rezaei¹,

Agahi²

2017

SIPIJ

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“…[12] It is essential to diminish the amount of features to drop off the computation time and the necessary storage. Principal component analysis (PCA) is an excellent technique used for modifying input feature into a novel one through inferior dimension.PCA uses the major eigenvectors of the correlation matrix to restructure features according to their divergences [8].…”

Section: Feature Selectionmentioning

confidence: 99%

Classification of Brain Tumor using GLCM and SVM from MRI Images

2017

Dec. 7-8, 2017 Paris (France) CCCME-17, MAIE-17, CAEBS-17, L2hss-17 &Amp; LEBHM-17

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“…Classification is done by using the supervised neural network called the Radial Basis Function (RBF), Generalised Regression neural network (GRNN), Probabilistic neural network (PNN) and Radial Basis Function neural network produces an accuracy of about 91.31 %, Generalised regression neural network provides an accuracy of 96.31 % , probabilistic neural network provides an accuracy of about 97.29%. Mahmoud Khaled Abd-Ellah et al [19] worked on "Design and Implementation of a Computer-Aided Diagnosis System for Brain Tumor Classification.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review on Performance Evaluation of Image Processing Technique for Brain Tumor Detection

2017

IJMTER

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Brain tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and characteristics and have different treatments. Detection of tumor in the earlier stages makes the treatment easier. Presently many medical imaging techniques such as positron emission tomography (PET), X-ray, computed tomography (CT), magnetic resonance imaging (MRI) for tumor detection are in use among which MRI imaging technique is more attractive because of its higher resolution and therefore most preferred technique for diagnosing tumor. MRI scan is safer than any other scan as it does not involve any radiations and gives high resolution of image. Paper gives a brief review of different segmentation and classification methods used for detection of tumor from Magnetic Resonance Images (MRI).

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Design and implementation of a computer-aided diagnosis system for brain tumor classification

Cited by 63 publications

References 17 publications

Malignant and Benign Brain Tumor Segmentation and Classification Using SVM with Weighted Kernel Width

Malignant and Benign Brain Tumor Segmentation and Classification Using SVM with Weighted Kernel Width

Classification of Brain Tumor using GLCM and SVM from MRI Images

A Review on Performance Evaluation of Image Processing Technique for Brain Tumor Detection

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