Combining optimal wavelet statistical texture and recurrent neural network for tumour detection and classification over MRI

Brain tumor is one of the major causes of death among other types of the cancer because brain is a very sensitive, complex and central part of the body. Proper and timely diagnosis can prevent the life of a person to some extent. Therefore, in this paper, an efficient brain tumor detection system is proposed using combining optimal wavelet statistical texture features and recurrent neural network (RNN). The proposed system consists of four phases namely; feature extraction feature selection, classification and segmentation. First, noise removal is performed as the preprocessing step on the brain MR images. After that, texture features (both the dominant run length and co-occurrence texture features) are extracted from these noise free MR images. The high number of features is reduced based on oppositional gravitational search algorithm (OGSA). Then, selected features are given to the Recurrent Neural Network (RNN) classifier to classify an image as normal or abnormal. After the classification process, abnormal images are given to the segmentation stage to segment the ROI region with the help of modified region growing algorithm (MRG). The performance of the proposed methodology is analyzed in terms of different metrics and experimental results are compared with existing methods.

[1]  Thomas G. Dietterich,et al.  Learning with Many Irrelevant Features , 1991, AAAI.

[2]  Alireza Osareh,et al.  A Computer Aided Diagnosis System for Breast Cancer , 2011 .

[3]  Nurettin Acir,et al.  Automatic classification of auditory brainstem responses using SVM-based feature selection algorithm for threshold detection , 2006, Eng. Appl. Artif. Intell..

[4]  B. Vijayakumar,et al.  Brain Tumour Mr Image Segmentation and Classification Using by PCA and RBF Kernel Based Support Vector Machine , 2015 .

[5]  A Horsman,et al.  Tumour volume determination from MR images by morphological segmentation , 1996, Physics in medicine and biology.

[6]  Yu Zhang,et al.  Automated defect recognition of C-SAM images in IC packaging using Support Vector Machines , 2005 .

[7]  A. Padma Nanthagopal,et al.  Wavelet statistical texture features-based segmentation and classification of brain computed tomography images , 2013, IET Image Process..

[8]  Sanjay Sharma,et al.  Brain Tumor Detection based on Multi-parameter MRI Image Analysis , 2009 .

[9]  Mehmet Fatih Akay,et al.  Support vector machines combined with feature selection for breast cancer diagnosis , 2009, Expert Syst. Appl..

[10]  Russell Greiner,et al.  Quick detection of brain tumors and edemas: A bounding box method using symmetry , 2012, Comput. Medical Imaging Graph..

[11]  Neil M. Borden.  Pattern Recognition Neuroradiology , 2011 .

[12]  Guido Gerig,et al.  A brain tumor segmentation framework based on outlier detection , 2004, Medical Image Anal..

[13]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[14]  Shivani Khurana,et al.  Brain Tumor Detection Using Neural Network , 2022 .

[15]  Aik Choon Tan,et al.  Ensemble machine learning on gene expression data for cancer classification. , 2003, Applied bioinformatics.

[16]  M. Cevdet Ince,et al.  An expert system for detection of breast cancer based on association rules and neural network , 2009, Expert Syst. Appl..

[17]  Vinod Kumar,et al.  A novel content-based active contour model for brain tumor segmentation. , 2012, Magnetic resonance imaging.

[18]  A. Levine,et al.  Gene assessment and sample classification for gene expression data using a genetic algorithm/k-nearest neighbor method. , 2001, Combinatorial chemistry & high throughput screening.

[19]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[20]  M. Ringnér,et al.  Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.

[21]  Jun Zhang,et al.  Tumor Classification Using Eigengene-Based Classifier Committee Learning Algorithm , 2012, IEEE Signal Processing Letters.

[22]  Elena Marchiori,et al.  Ensemble Feature Ranking , 2004, PKDD.

[23]  Gözde B. Ünal,et al.  Tumor-Cut: Segmentation of Brain Tumors on Contrast Enhanced MR Images for Radiosurgery Applications , 2012, IEEE Transactions on Medical Imaging.

[24]  D. Sridhar,et al.  Brain Tumor Classification using Discrete Cosine Transform and Probabilistic Neural Network , 2013, 2013 International Conference on Signal Processing , Image Processing & Pattern Recognition.

[25]  S. Dudoit,et al.  Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .

[26]  Qianjin Feng,et al.  Brain Tumor Segmentation Based on Local Independent Projection-Based Classification , 2014, IEEE Transactions on Biomedical Engineering.

[27]  Jau-Min Wong,et al.  Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing , 2011, BMC Medical Informatics Decis. Mak..

[28]  Sayali Patil,et al.  SEGMENTATION OF BRAIN TUMOUR AND ITS AREA CALCULATION IN BRAIN MR IMAGES USING K-MEAN CLUSTERING AND FUZZY C- MEAN ALGORITHM , 2014 .

[29]  Srinivasalu Preethi,et al.  Combining Wavelet Texture Features and Deep Neural Network for Tumor Detection and Segmentation Over MRI , 2019, J. Intell. Syst..

[30]  Tae-Sun Choi,et al.  Fuzzy anisotropic diffusion based segmentation and texture based ensemble classification of brain tumor , 2014, Appl. Soft Comput..

[31]  Giorgio Valentini,et al.  Cancer recognition with bagged ensembles of support vector machines , 2004, Neurocomputing.

[32]  Kailash D. Kharat,et al.  Brain Tumor Classification Using Neural Network Based Methods , 2012 .

[33]  Sung-Bae Cho,et al.  Machine Learning in DNA Microarray Analysis for Cancer Classification , 2003, APBC.

[34]  Sim Heng Ong,et al.  Level-set segmentation of brain tumors using a threshold-based speed function , 2010, Image Vis. Comput..

[35]  Qianjin Feng,et al.  D brain tumor segmentation in multimodal MR images based on earning population-and patient-specific feature sets , 2013 .

[36]  Qingmao Hu,et al.  Rapid and automatic detection of brain tumors in MR images , 2004, SPIE Medical Imaging.

[37]  Debnath Bhattacharyya,et al.  Brain Tumor Detection Using MRI Image Analysis , 2011, UCMA.

[38]  D. Selvathi,et al.  Tumor Detection in Brain Magnetic Resonance Images Using Modified Thresholding Techniques , 2011, ACC.

[39]  C. Chellamuthu,et al.  Brain tumour detection using self-adaptive learning PSO-based feature selection algorithm in MRI images , 2019, Int. J. Bus. Intell. Data Min..

[40]  Tamer Ölmez,et al.  Tumor detection by using Zernike moments on segmented magnetic resonance brain images , 2010, Expert Syst. Appl..

[41]  Isaac N. Bankman,et al.  Handbook of medical image processing and analysis , 2009 .