Affiliation:
1. Department of Computer Sciences & Information Technology, University of Azad Jammu and Kashmir, City Campus 13100, Muzaffarabad, Azad Kashmir, Pakistan
2. Department of Computer Sciences & Information Technology, University of Poonch Rawalakot, Rawalakot, Pakistan
Abstract
Background:
Brain tumor is the leading cause of death worldwide. It is obvious that the
chances of survival can be increased if the tumor is identified and properly classified at an initial
stage. MRI (Magnetic Resonance Imaging) is one source of brain tumors detection tool and is extensively
used in the diagnosis of brain to detect blood clots. In the past, many researchers developed
Computer-Aided Diagnosis (CAD) systems that help the radiologist to detect the abnormalities
in an efficient manner.
Objective:
The aim of this research is to improve the brain tumor detection performance by proposing
a multimodal feature extracting strategy and employing machine learning techniques.
Methods:
In this study, we extracted multimodal features such as texture, morphological, entropybased,
Scale Invariant Feature Transform (SIFT), and Elliptic Fourier Descriptors (EFDs) from
brain tumor imaging database. The tumor was detected using robust machine learning techniques
such as Support Vector Machine (SVM) with kernels: polynomial, Radial Base Function (RBF),
Gaussian; Decision Tree (DT), and Naïve Bayes. Most commonly used Jack-knife 10-fold Cross-
Validation (CV) was used for testing and validation of dataset.
Results:
The performance was evaluated in terms of specificity, sensitivity, Positive Predictive
Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), Total Accuracy (TA),
Area under the receiver operating Curve (AUC), and P-value. The highest performance of 100% in
terms of Specificity, Sensitivity, PPV, NPV, TA, AUC using Naïve Bayes classifiers based on entropy,
morphological, SIFT and texture features followed by Decision Tree classifier with texture
features (TA=97.81%, AUC=1.0) and SVM polynomial kernel with texture features (TA=94.63%).
The highest significant p-value was obtained using SVM polynomial with texture features (P-value
2.65e-104) followed by SVM RB with texture features (P-value 1.96e-98).
Conclusion:
The results reveal that Naïve Bayes followed by Decision Tree gives highest detection
accuracy based on entropy, morphological, SIFT and texture features.
Publisher
Bentham Science Publishers Ltd.
Subject
Radiology, Nuclear Medicine and imaging
Reference104 articles.
1. Gaikwad SB, Joshi MS. Brain tumor classification using principal component analysis and probabilistic neural network.
2. Rathi VPGP, Palani S. Brain tumor MRI image classification with feature selection and extration using linear discriminant analysis.
3. Naik J, Patel S. Tumor detection and classification using decision tree in brain MRI.
4. Prabin A, Veerappan J. Robust classification of primary brain tumor in MRI images based on multi model textures features and kernel based SVM.
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