Brain tumor MRI identification and classification using DWT, PCA and kernel support vector machine
DOI:
https://doi.org/10.47813/2782-2818-2024-4-1-0133-0152Keywords:
classification methods, Discrete Wavelet Transform, Feature extraction, Image Segmentation, Pre-Processing, Probabilistic Neural Network, convolution Neural NetworkAbstract
Classification, segmentation, and the identification of the infection region in MRI images of brain tumors are labor-intensive and iterative processes. Numerous anatomical structures of the human body may be envisioned using an image processing theory. With basic imaging methods, it is challenging to see the aberrant human brain's structure. The neurological structure of the human brain may be distinguished and made clearer using the magnetic resonance imaging technique. The MRI approach uses a number of imaging techniques to evaluate and record the human brain’s interior features. In this study, we focused on strategies for noise removal, gray-level co-occurrence matrix (GLCM) extraction of features, and segmentation of brain tumor regions based on Discrete Wavelet Transform (DWT) to minimize complexity and enhance performance. In turn, this reduces any noise that could have been left over after segmentation due to morphological filtering. Brain MRI scans were utilized to test the accuracy of the classification and the location of the tumor using probabilistic neural network classifiers. The classifier's accuracy and position detection were tested using MRI brain imaging. The efficiency of the suggested approach is demonstrated by experimental findings, which showed that normal and diseased tissues could be distinguished from one another from brain MRI scans with about 100% accuracy.
References
Varuna Shree N., Kumar T. N. R. Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Informatics. 2018; 5(1): 23-30. https://doi.org/10.1007/s40708-017-0075-5 DOI: https://doi.org/10.1007/s40708-017-0075-5
Sharma A., Singh J. Image denoising using spatial domain filters: A quantitative study. 2013 6th International Congress on Image and Signal Processing (CISP), Dec. 2013. https://doi.org/10.1109/cisp.2013.6744005 DOI: https://doi.org/10.1109/CISP.2013.6744005
Salem Ghahfarrokhi S., Khodadadi H. Human brain tumor diagnosis using the combination of the complexity measures and texture features through magnetic resonance image. Biomedical Signal Processing and Control, 2020; 61: 102025. https://doi.org/10.1016/j.bspc.2020.102025 DOI: https://doi.org/10.1016/j.bspc.2020.102025
Kwak N., Choi Chong-H. Input feature selection for classification problems. IEEE Transactions on Neural Networks. 2002; 13(1): 143-159. https://doi.org/10.1109/72.977291 DOI: https://doi.org/10.1109/72.977291
Amin J., Sharif M., Raza M., Saba T., Rehman A. Brain Tumor Classification: Feature Fusion. 2019 International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia. 2019: 1-6. https://doi.org/10.1109/ICCISci.2019.8716449 DOI: https://doi.org/10.1109/ICCISci.2019.8716449
Ayadi W., Elhamzi W., Charfi I., Atri M. A hybrid feature extraction approach for brain MRI classification based on Bag-of-words. Biomedical Signal Processing and Control. 2019; 48: 144-152. https://doi.org/10.1016/j.bspc.2018.10.010 DOI: https://doi.org/10.1016/j.bspc.2018.10.010
Nagtode S. A., Potdukhe B. B., Morey P. Two dimensional discrete Wavelet transform and Probabilistic neural network used for brain tumor detection and classification. In 2016 Fifth International Conference on Eco-friendly Computing and Communication Systems (ICECCS). 2016. https://doi.org/10.1109/eco-friendly.2016.7893235 DOI: https://doi.org/10.1109/Eco-friendly.2016.7893235
Mathew A.R., Anto P.B. Tumor detection and classification of MRI brain image using wavelet transform and SVM. 2017 International Conference on Signal Processing and Communication (ICSPC). 2017: 75-78. https://doi.org/10.1109/CSPC.2017.8305810 DOI: https://doi.org/10.1109/CSPC.2017.8305810
Mao K. Z., Tan K.-C., Ser W. Probabilistic neural-network structure determination for pattern classification. IEEE Transactions on Neural Networks. 2000; 11(4): 1009-1016. https://doi.org/10.1109/72.857781 DOI: https://doi.org/10.1109/72.857781
Bahadure N.B., Ray A.K., Thethi H.P. Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM. International Journal of Biomedical Imaging. 2017; 2017: 1-12. https://doi.org/10.1155/2017/9749108 DOI: https://doi.org/10.1155/2017/9749108
Sabitha V., Nayak J., Reddy P. R. MRI brain tumor detection and classification using KPCA and KSVM. Materials Today: Proceedings. 2021. https://doi.org/10.1016/j.matpr.2021.01.714 DOI: https://doi.org/10.1016/j.matpr.2021.01.714
Rao C.S., Karunakara K. Efficient Detection and Classification of Brain Tumor using Kernel based SVM for MRI. Multimedia Tools and Applications. 2022. https://doi.org/10.1007/s11042-021-11821-z DOI: https://doi.org/10.1007/s11042-021-11821-z
Kumar K, Devi K.T. An Efficient Method for Brain Tumor Detection Using Texture Features and SVM Classifier in MR Images. Asian Pac J Cancer Prev. 2018; 19(10): 2789-2794. https://doi.org/10.22034/APJCP.2018.19.10.2789
Mandle A. K., Sahu S. P., Gupta G. Brain Tumor Segmentation and Classification in MRI using Clustering and Kernel-Based SVM. Biomedical and Pharmacology Journal. 2022; 15(2): 699-716. https://doi.org/10.13005/bpj/2409 DOI: https://doi.org/10.13005/bpj/2409
Jahidul I.M., Faruq O. Further Exploration of Deep Aggregation for Shadow Detection. Modern Innovations, Systems and Technologies. 2022; 2(3): 0312-0330. https://doi.org/10.47813/2782-2818-2022-2-3-0312-0330 DOI: https://doi.org/10.47813/2782-2818-2022-2-3-0312-0330
Naceur M. B., Saouli R., Akil M., Kachouri R. Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images. Computer Methods and Programs in Biomedicine. 2018; 166: 39-49. https://doi.org/10.1016/j.cmpb.2018.09.007 DOI: https://doi.org/10.1016/j.cmpb.2018.09.007
Lavanyadevi R., Machakowsalya M., Nivethitha J., Kumar A.N. Brain tumor classification and segmentation in MRI images using PNN. 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE), Apr. 2017. https://doi.org/10.1109/iceice.2017.8191888 DOI: https://doi.org/10.1109/ICEICE.2017.8191888
Othman M.F., Basri M.A.M. Probabilistic Neural Network for Brain Tumor Classification. 2011 Second International Conference on Intelligent Systems, Modelling and Simulation. 2011: 136-138. https://doi.org/10.1109/ISMS.2011.32 DOI: https://doi.org/10.1109/ISMS.2011.32
Deepa B., Sumithra M. G., Kumar R. M., Suriya M. Weiner Filter based Hough Transform and Wavelet feature extraction with Neural Network for Classifying Brain Tumor. 2021 6th International Conference on Inventive Computation Technologies (ICICT), Jan. 2021. https://doi.org/10.1109/icict50816.2021.9358680 DOI: https://doi.org/10.1109/ICICT50816.2021.9358680
Thara S., Jasmine K. Brain tumour detection in MRI images using PNN and GRNN. 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Mar. 2016. https://doi.org/10.1109/wispnet.2016.7566388 DOI: https://doi.org/10.1109/WiSPNET.2016.7566388
Zhang Y., Wang S., Wu L. A novel method for magnetic resonance brain image classification based on adaptive chaotic pso. Electromagn. Waves (Camb.). 2010; 109: 325-343. http://dx.doi.org/10.2528/PIER10090105 DOI: https://doi.org/10.2528/PIER10090105
Das S., Siddiqui N., Kriti N., Tamang S. P. Detection and area calculation of brain tumour from MRI images using MATLAB. International Journal of Computer Engineering In Research Trends. 2017; 4(1): 37-40. Available:
https://www.semanticscholar.org/paper/d7f3b185dcc8ed23f862b98a221988418e88c53f
Saraswathi D., Priya B. L., Punitha Lakshmi R. Brain tumor segmentation and classification using self-organizing map. In 2019 IEEE international conference on system, computation, automation and networking (ICSCAN). IEEE. 2019: 1-5. http://dx.doi.org/10.1109/ICSCAN.2019.8878763 DOI: https://doi.org/10.1109/ICSCAN.2019.8878763
Amin J., Sharif M., Raza M., Saba T., Anjum M. A. Brain tumor detection using statistical and machine learning method. Computer Methods and Programs in Biomedicine. 2019; 177: 69-79. https://doi.org/10.1016/j.cmpb.2019.05.015 DOI: https://doi.org/10.1016/j.cmpb.2019.05.015
Pandiselvi T., Maheswaran R. Efficient Framework for Identifying, Locating, Detecting and Classifying MRI Brain Tumor in MRI Images. Journal of Medical Systems. 2019; 43(7). https://doi.org/10.1007/s10916-019-1253-1 DOI: https://doi.org/10.1007/s10916-019-1253-1
Faruq O., Jahi I., Ahmed M. S., Hossain M. S. Brain tumor MRI identification and classification using DWT, PCA, and KSVM. Springer Research Square. 2023. https://doi.org/10.36227/techrxiv.21771329.v2 DOI: https://doi.org/10.21203/rs.3.rs-2562932/v1
Faruq O., Islam M. J., Ahmed M. S., Hossain M. S. Brain tumor MRI identification and classification using DWT, PCA, and KSVM. IEEE TechRxiv. 2023. https://doi.org/10.21203/rs.3.rs-2562932/v1 DOI: https://doi.org/10.36227/techrxiv.21771329.v2
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