ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Performance Analysis of SVM and Deep Learning with CNN for Brain Tumor Detection and Classification


Affiliations
1 Department of Electrical and Electronics Engineering, PSG College of Technology, India
     

   Subscribe/Renew Journal


A brain tumor occurs when abnormal cells form within the brain. In diagnosis of the disease medical imaging has many advantages. Many people suffer from brain tumor, it is a serious and dangerous disease. A proper diagnosis of brain tumor is provided by the medical imaging. The detection and classification of tumor from brain is an important and difficult task in the medical field. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. Tumor detection and classification are very hard because of high quantity of data in MRI images. One essential part in detecting the tumor is image segmentation. The segmentation provides an automatic brain tumor detection technique in order to increase the precision, yields with decrease in the diagnosis time. The goal is to detect the tumor from the MRI images and extract the features from the segmented tumor and finally classify it. The image detection and classification include image acquisition, image preprocessing, denoising, image segmentation, feature extraction and classification. The input image is pre-processed using wiener filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it is used for segmentation process, where Mean Shift Clustering is used. The segmented tumor undergoes features extraction stage, where Gray Level Co-occurrence Matrix (GLCM) features are used. In the last stage images are classified either as tumorous or non-tumorous. Classification is done using Support Vector Machine (SVM), Deep Learning with Convolutional Neural Network (CNN). Early detection of the tumor region can be achieved without much time lapse in the calculation by using this efficient classifier model. This system presents a prototype for detecting objects based on SVM that classifies images and assesses whether the image is cancerous. While comparing the accuracy of these classifier, CNN would provide high accuracy. The simulation results obtained for brain tumor detection and analysis are done with minimum computational time and with reasonable accuracy. This proposed system is tested using PSGIMSR (PSG Hospitals, Coimbatore) dataset and implemented using MATLAB software.

Keywords

Wiener filter, Edge Adaptive Total Variation Denoising, Gray Level Co-occurrence Matrix, Support Vector Machine, Convolutional Neural Network.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Ramesh Babu Vallabhaneni and V. Rajesh, “Brain Tumor Detection using Mean Shift Clustering and GLCM Features with Edge Adaptive Total Variation Denoising Technique”, ARPN Journal of Engineering and Applied Sciences, Vol. 12, No. 3, pp. 666-671, 2018.

  • T. Chithambaram and K. Perumal, “Brain Tumor Segmentation using Genetic Algorithm and ANN Techniques”, Proceedings of IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, pp. 1-6, 2018.

  • T. Chithambaram and K. Perumal, “Brain Tumor Detection and Segmentation in MRI Images using Neural Network”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 7, No 3, pp. 1-12, 2017, pp. 155-164.

  • Megha Kadam and Avinash Dhole, “Brain Tumor Detection using GLCM with the help of KSVM”, International Journal of Engineering and Technical Research, Vol.7, No. 2, pp. 31-39, 2017.

  • G.B. Praveen and Anita Agrawal, “Multistage Classification and Segmentation of Brain Tumor”, Proceedings of IEEE International Conference on Computing for Sustainable Global Development, pp. 1-7, 2016.

  • D. Haritha, “Brain Tumour Segmentation”, International Journal of Advanced Technology in Engineering and Science, Vol. 4, No. 3, pp. 265-270, 2016.

  • S. Goswami and L.K.P. Bhaiya, “A Hybrid Neuro-Fuzzy Approach for Brain Abnormality Detection using GLCM Based Feature Extraction”, Proceedings of International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, pp. 1-7, 2013.

  • A.R. Matthew, A. Prasad and P.B. Anto, “A Review on Feature Extraction Techniques for Tumour Detection and Classification from Brain MRI”, Proceedings of International Conference on Intelligent Computing, Instrumentation and Control Technologies, pp. 1766-1771, 2017.

  • W. Xu, X. Yue, Y. Chen and M. Reformat, “Ensemble of Active Contour based Image Segmentation”, Proceedings of IEEE International Conference on Image Processing, pp. 86-90, 2017.

  • D.R. Byreddy and M. Raghunadh, “An Application of Geometric Active Contour in Bio-Medical Engineering”, Proceedings of International Conference on Circuits, Systems, Communication and Information Technology Applications, pp. 322-326, 2014.

  • Ruchi D. Deshmukh and Chaya Jadhav, “Study of Different Brain Tumor MRI Image Segmentation Techniques”, International Journal of Computer Science Engineering and Technology, Vol. 4, No. 4, pp. 133-136, 2014.

  • A.R. Kavitha, L. Chitra and R. Kanaga, “Brain Tumor Segmentation using Genetic Algorithm with SVM Classifier”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 5, No. 3, pp. 1468-1471, 2016.

  • R.J. Ramteke and Y. Khachane Monali, “Automatic Medical Image Classification and Abnormality Detection Using K-Nearest Neighbor”, International Journal of Advanced Computer Research, Vol. 2, No. 4, pp. 190-196, 2012.

  • Aurav Gupta and Vinay Singh, “Brain Tumor Segmentation and Classification using FCM and Support Vector Machine”, International Research Journal of Engineering and Technology, Vol. 4, No. 5, pp. 792-796, 2017.

  • N. Vani, A. Sowmya and N. Jayamma, “Brain Tumor Classification using Support Vector Machine”, International Research Journal of Engineering and Technology, Vol. 4, No. 7, pp. 1724-1729, 2017.

  • S.U Aswathy, G. Glan Devadhas and S.S. Kumar, “MRI Brain Tumor Segmentation using Genetic Algorithm with SVM Classifier”, Proceedings of National Symposium on Antenna Signal Processing and Interdisciplinary Research, pp. 22-26, 2017.

  • Narmada M. Balasooriya and Ruwan D. Nawarathna, “A Sophisticated Convolutional Neural Network Model for Brain Tumor Classification”, Proceedings of IEEE International Conference on Industrial and Information Systems, pp. 1-5, 2017.

  • P. Chinmayi and L. Agilandeeswari, “An Efficient Deep Learning Neural Network based Brain Tumor Detection System”, International Journal of Pure and Applied Mathematics, Vol. 117, No. 17, pp. 1-12, 2017.

  • Ketan Machhale and Hari Babu Nandpuru, “MRI Brain Cancer Classification using Hybrid Classifier (SVM-KNN)”, Proceedings of International Conference on Industrial Instrumentation and Control, pp. 1-7, 2015.

  • M.F.B. Othman, N.B. Abdullah and N.F.B. Kamal, “MRI Brain Classification using Support Vector Machine”, Proceedings of 4th International Conference on Modelling, Simulation and Applied Optimization, pp. 1-4, 2011.


Abstract Views: 151

PDF Views: 0




  • Performance Analysis of SVM and Deep Learning with CNN for Brain Tumor Detection and Classification

Abstract Views: 151  |  PDF Views: 0

Authors

D. M. Mahalakshmi
Department of Electrical and Electronics Engineering, PSG College of Technology, India
S. Sumathi
Department of Electrical and Electronics Engineering, PSG College of Technology, India

Abstract


A brain tumor occurs when abnormal cells form within the brain. In diagnosis of the disease medical imaging has many advantages. Many people suffer from brain tumor, it is a serious and dangerous disease. A proper diagnosis of brain tumor is provided by the medical imaging. The detection and classification of tumor from brain is an important and difficult task in the medical field. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. Tumor detection and classification are very hard because of high quantity of data in MRI images. One essential part in detecting the tumor is image segmentation. The segmentation provides an automatic brain tumor detection technique in order to increase the precision, yields with decrease in the diagnosis time. The goal is to detect the tumor from the MRI images and extract the features from the segmented tumor and finally classify it. The image detection and classification include image acquisition, image preprocessing, denoising, image segmentation, feature extraction and classification. The input image is pre-processed using wiener filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it is used for segmentation process, where Mean Shift Clustering is used. The segmented tumor undergoes features extraction stage, where Gray Level Co-occurrence Matrix (GLCM) features are used. In the last stage images are classified either as tumorous or non-tumorous. Classification is done using Support Vector Machine (SVM), Deep Learning with Convolutional Neural Network (CNN). Early detection of the tumor region can be achieved without much time lapse in the calculation by using this efficient classifier model. This system presents a prototype for detecting objects based on SVM that classifies images and assesses whether the image is cancerous. While comparing the accuracy of these classifier, CNN would provide high accuracy. The simulation results obtained for brain tumor detection and analysis are done with minimum computational time and with reasonable accuracy. This proposed system is tested using PSGIMSR (PSG Hospitals, Coimbatore) dataset and implemented using MATLAB software.

Keywords


Wiener filter, Edge Adaptive Total Variation Denoising, Gray Level Co-occurrence Matrix, Support Vector Machine, Convolutional Neural Network.

References