Brain Tumor Grade Detection using Multi Level Weighted Group Feature Set Based Dissimilar Region Detection using Machine Learning Technique
Keywords:
Brain Tumor, Tumor Grade, Magnetic Resonance Imaging, Feature Extraction, Weight Allocation, Dissimilar Region DetectionAbstract
Brain tumours are disreputably dangerous and difficult to treat. Brain tumours are identified by a laborious and error-prone process of manual visual inspection of pictures and manual marking of the suspicious areas by medical specialists. Magnetic Resonance (MR) images have had their ambiguity resolved in a more straightforward fashion. The work analyses the MRI images considered from public dataset providers. In recent years, researchers have proposed automating ways to detect brain cancers at an early stage. The tumour is a common malignant development with atypical features. Tumors of the brain are a form of abnormal growth of tissue in which cells multiply rapidly and out of control. Nature, origin, development rate, and maturity level are used to define its many varieties. Traditional methods of tumour detection are laborious, limited in their ability to effectively process vast amounts of data, and inaccurate. Hence, computer-aided diagnosis relies heavily on MRI's ability to automatically detect brain cancers. Variations in tumour location, shape, and size present a significant obstacle for brain tumour detection. The importance of early detection of brain cancers cannot be overstated. Methods based on computational intelligence can aid in the diagnosis and categorization of brain tumours with accurate grade detection that helps in proper treatment. To aid doctors in the early detection of malignancies with accurate grade detection, a Multi Level Weighted Group Feature Set based Dissimilar Region Detection (WGFS-DRD) for accurate grade detection in brain tumor detection. The proposed model is compared with the traditional models and the results represents that the proposed model performance is high.
Downloads
References
B. Deepa, M. Murugappan, M. G. Sumithra, M. Mahmud and M. S. Al-Rakhami, "Pattern Descriptors Orientation and MAP Firefly Algorithm Based Brain Pathology Classification Using Hybridized Machine Learning Algorithm," in IEEE Access, vol. 10, pp. 3848-3863, 2022, doi: 10.1109/ACCESS.2021.3100549.
M. Rizwan, A. Shabbir, A. R. Javed, M. Shabbir, T. Baker and D. Al-Jumeily Obe, "Brain Tumor and Glioma Grade Classification Using Gaussian Convolutional Neural Network," in IEEE Access, vol. 10, pp. 29731-29740, 2022, doi: 10.1109/ACCESS.2022.3153108.
Z. A. Al-Saffar and T. Yildirim, "A Novel Approach to Improving Brain Image Classification Using Mutual Information-Accelerated Singular Value Decomposition," in IEEE Access, vol. 8, pp. 52575-52587, 2020, doi: 10.1109/ACCESS.2020.2980728.
A.Sekhar, S. Biswas, R. Hazra, A. K. Sunaniya, A. Mukherjee and L. Yang, "Brain Tumor Classification Using Fine-Tuned GoogLeNet Features and Machine Learning Algorithms: IoMT Enabled CAD System," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 3, pp. 983-991, March 2022, doi: 10.1109/JBHI.2021.3100758.
Vidyarthi, R. Agarwal, D. Gupta, R. Sharma, D. Draheim and P. Tiwari, "Machine Learning Assisted Methodology for Multiclass Classification of Malignant Brain Tumors," in IEEE Access, vol. 10, pp. 50624-50640, 2022, doi: 10.1109/ACCESS.2022.3172303.
N. Noreen, S. Palaniappan, A. Qayyum, I. Ahmad, M. Imran and M. Shoaib, "A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor," in IEEE Access, vol. 8, pp. 55135-55144, 2020, doi: 10.1109/ACCESS.2020.2978629.
S. Ahmad and P. K. Choudhury, "On the Performance of Deep Transfer Learning Networks for Brain Tumor Detection Using MR Images," in IEEE Access, vol. 10, pp. 59099-59114, 2022, doi: 10.1109/ACCESS.2022.3179376.
Y. Zhuang, H. Liu, E. Song and C. -C. Hung, "A 3D Cross-Modality Feature Interaction Network With Volumetric Feature Alignment for Brain Tumor and Tissue Segmentation," in IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 1, pp. 75-86, Jan. 2023, doi: 10.1109/JBHI.2022.3214999.
Deepa, M. Murugappan, M. G. Sumithra, M. Mahmud and M. S. Al-Rakhami, "Pattern Descriptors Orientation and MAP Firefly Algorithm Based Brain Pathology Classification Using Hybridized Machine Learning Algorithm," in IEEE Access, vol. 10, pp. 3848-3863, 2022, doi: 10.1109/ACCESS.2021.3100549.
K. Venkatachalam, S. Siuly, N. Bacanin, S. Hubálovský and P. Trojovský, "An Efficient Gabor Walsh-Hadamard Transform Based Approach for Retrieving Brain Tumor Images From MRI," in IEEE Access, vol. 9, pp. 119078-119089, 2021, doi: 10.1109/ACCESS.2021.3107371.
M. I. Razzak, M. Imran and G. Xu, "Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks", IEEE J. Biomed. Health Informat., vol. 23, no. 5, pp. 1911-1919, Sep. 2019.
Lei, P. Yang, Y. Zhuo, F. Zhou, D. Ni, S. Chen, et al., "Neuroimaging retrieval via adaptive ensemble manifold learning for brain disease diagnosis", IEEE J. Biomed. Health Informat., vol. 23, no. 4, pp. 1661-1673, Jul. 2019.
N. Abiwinanda, M. Hanif, S. T. Hesaputra, A. Handayani and T. R. Mengko, "Brain tumor classification using convolutional neural network" in World Congress on Medical Physics and Biomedical Engineering, Bhopal, India:Springer, pp. 183-189, 2019.
T. Meraj, A. Hassan, S. Zahoor, H. T. Rauf, M. I. Lali, L. Ali, et al., "Lungs nodule detection using semantic segmentation and classification with optimal features", Neural Comput. Appl., vol. 33, no. 17, pp. 10737-10750, 2019.
S. Kadry, Y. Nam, H. T. Rauf, V. Rajinikanth and I. A. Lawal, "Automated detection of brain abnormality using deep-learning-scheme: A study", Proc. 7th Int. Conf. Bio Signals Images Instrum. (ICBSII), pp. 1-5, Mar. 2021.
M. Chen, F. Herrera and K. Hwang, "Cognitive computing: Architecture technologies and intelligent applications", IEEE Access, vol. 6, pp. 19774-19783, 2018.
T. Hossain, F. Shishir and M. Ashraf, "Brain tumor detection using CNN", Turkish J. Comput. Math. Educ., vol. 12, no. 11, pp. 4597-4603, 2021.
P. R. Krishnaveni and G. N. Kishore, "Image based group classifier for brain tumor detection using machine learning technique", Traitement Signal, vol. 37, no. 5, pp. 865-871, Nov. 2020.
J. Amin, M. Sharif, M. Raza, T. Saba and M. A. Anjum, "Brain tumor detection using statistical and machine learning method", Comput. Methods Programs Biomed., vol. 177, pp. 69-79, Aug. 2019.
K. Muhammad, S. Khan, J. D. Ser and V. H. C. D. Albuquerque, "Deep learning for multigrade brain tumor classification in smart healthcare systems: A prospective survey", IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 2, pp. 507-522, Feb. 2021.
Y. Wang, W. Kinsner, S. Kwong, H. Leung, J. Lu, M. H. Smith, et al., "Brain-inspired systems: A transdisciplinary exploration on cognitive cybernetics humanity and systems science toward autonomous artificial intelligence", IEEE Syst. Man Cybern. Mag., vol. 6, no. 1, pp. 6-13, Jan. 2020.
Irmak, "Multi-classification of brain tumor mri images using deep convolutional neural network with fully optimized framework", Iranian J. Sci. Technol. Trans. Electr. Eng., vol. 4, pp. 1-22, Oct. 2021.
P. R. Kshirsagar, A. N. Rakhonde and P. Chippalkatti, "MRI image based brain tumor detection using machine learning", Test Eng. Manage., vol. 4, pp. 3672-3680, Jan. 2020.
C. Ma, G. Luo and K. Wang, "Concatenated and connected random forests with multiscale patch driven active contour model for automated brain tumor segmentation of MR images", IEEE Trans. Med. Imag., vol. 37, no. 8, pp. 1943-1954, Aug. 2018.
C.-W. Zhang, M.-Y. Yang, H.-J. Zeng and J.-P. Wen, "Pedestrian detection based on improved LeNet-5 convolutional neural network", J. Algorithms Comput. Technol., vol. 13, Sep. 2019.
Zhang, G. Huang, Q. Zhang, J. Han, J. Han and Y. Yu, "Cross-modality deep feature learning for brain tumor segmentation", Pattern Recognit., vol. 110, Mar. 2021.
P. M. Shakeel, T. E. El Tobely, H. Al-Feel, G. Manogaran and S. Baskar, "Neural network based brain tumor detection using wireless infrared imaging sensor", IEEE Access, vol. 7, pp. 5577-5588, 2019.
Kutlu, "A novel method for classifying liver and brain tumors using convolutional neural networks discrete wavelet transform and long short-term memory networks", Sensors, vol. 19, no. 9, pp. 1992, Apr. 2019.
K. Anaraki, M. Ayati and F. Kazemi, "Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms", Biocybernetics Biomed. Eng., vol. 39, no. 1, pp. 63-74, Jan./Mar. 2019.
P. Afshar, K. N. Plataniotis and A. Mohammadi, "Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries", Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), pp. 1368-1372, May 2019.
Thomas Wilson, Andrew Evans, Alejandro Perez, Luis Pérez, Juan Martinez. Integrating Machine Learning and Decision Science for Effective Risk Management. Kuwait Journal of Machine Learning, 2(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/208
Sherje, N. P., Agrawal, S. A., Umbarkar, A. M., Dharme, A. M., & Dhabliya, D. (2021). Experimental evaluation of mechatronics based cushioning performance in hydraulic cylinder. Materials Today: Proceedings, doi:10.1016/j.matpr.2020.12.1021
Juan Garcia, Guðmundsdóttir Anna, Johansson Anna, Maria Jansen, Anna Wagner. Optimal Decision Making in Supply Chain Management using Machine Learning. Kuwait Journal of Machine Learning, 2(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/209
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
