Efficient Brain Tumor Identification Based on Optimal Support Scaling Vector Feature Selection (OSSCV) Using Stochastic Spin-Glass Model Classification
Keywords:
Machine learning, brain tumor, feature selection, classification, neural network, stochastic spin model, MRI image processingAbstract
Brain tumor detection is a developing defect finding task in medical imaging, as premature and early identification is a critical once for recommending early treatment. The tumor are identified by the laboratory through MRI images by finding the tumor regions. The Artificial intelligence play a vital role for finding, analyzing, the image data to attain the target results in medical image using various learning methodologies. Most of the existing system failed to find the find the feature dimension leads poor accuracy for identifying tumor regions due to low precision, recall rate, lower intensity in image coverage region. To resolve this problem, to propose an Optimal Support Scaling Vector Based Feature Selection (OSSCV) brain tumor identification using Stochastic Spin-Glass Model Classification (SSGM). Initially the preprocessing is done by bilateral filter and segmentation is applied by suing Active Region Slice Window Segmentation (ARSWS). To separate the tumor entity feature projection using Histogram color quantization and the features process are carried by Optimal Support Scaling Vector Based Feature Selection (OSSCV). The selected features get trained using Stochastic Spin-Glass Model Classification (SSGM) to find the tumor region. The proposed system outperforms traditional machine learning methods in brain tumor detection. Finally proposed system of Stochastic Spin-Glass Model (SSGM) performance of recall is 95.5%, the performance of F1-score is 96.1% and the performance of the 96.5%. The proposed approach has the potential to assist radiologists in diagnosing brain tumors more accurately and efficiently, leading to improved patient outcomes.
Downloads
References
S. Bauer, C. May, D. Dionysiou, G. Stamatakos, P. Buchler and M. Reyes, "Multiscale Modeling for Image Analysis of Brain Tumor Studies," in IEEE Transactions on Biomedical Engineering, vol. 59, no. 1, pp. 25-29, Jan. 2012, doi: 10.1109/TBME.2011.2163406.
A. Islam, S. M. S. Reza and K. M. Iftekharuddin, "Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors," in IEEE Transactions on Biomedical Engineering, vol. 60, no. 11, pp. 3204-3215, Nov. 2013, doi: 10.1109/TBME.2013.2271383. .
A Song, P. Wen, T. Ahfock and Y. Li, "Numeric Investigation of Brain Tumor Influence on the Current Distributions During Transcranial Direct Current Stimulation," in IEEE Transactions on Biomedical Engineering, vol. 63, no. 1, pp. 176-187, Jan. 2016, doi: 10.1109/TBME.2015.2468672.
A.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," in IEEE Transactions on Medical Imaging, vol. 37, no. 8, pp. 1943-1954, Aug. 2018, doi: 10.1109/TMI.2018.2805821.
A.I. Zacharaki, D. Shen, S. -K. Lee and C. Davatzikos, "ORBIT: A Multiresolution Framework for Deformable Registration of Brain Tumor Images," in IEEE Transactions on Medical Imaging, vol. 27, no. 8, pp. 1003-1017, Aug. 2008, doi: 10.1109/TMI.2008.916954.
M. Huang, W. Yang, Y. Wu, J. Jiang, W. Chen and Q. Feng, "Brain Tumor Segmentation Based on Local Independent Projection-Based Classification," in IEEE Transactions on Biomedical Engineering, vol. 61, no. 10, pp. 2633-2645, Oct. 2014, doi: 10.1109/TBME.2014.2325410.
T. A. Soomro et al., "Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review," in IEEE Reviews in Biomedical Engineering, vol. 16, pp. 70-90, 2023, doi: 10.1109/RBME.2022.3185292.
AHamamci, N. Kucuk, K. Karaman, K. Engin and G. Unal, "Tumor-Cut: Segmentation of Brain Tumors on Contrast-Enhanced MR Images for Radiosurgery Applications," in IEEE Transactions on Medical Imaging, vol. 31, no. 3, pp. 790-804, March 2012, doi: 10.1109/TMI.2011.2181857.
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.
S. Asif, W. Yi, Q. U. Ain, J. Hou, T. Yi and J. Si, "Improving Effectiveness of Different Deep Transfer Learning-Based Models for Detecting Brain Tumors From MR Images," in IEEE Access, vol. 10, pp. 34716-34730, 2022, doi: 10.1109/ACCESS.2022.3153306.
A.U. Haq et al., "IIMFCBM: Intelligent Integrated Model for Feature Extraction and Classification of Brain Tumors Using MRI Clinical Imaging Data in IoT-Healthcare," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 10, pp. 5004-5012, Oct. 2022, doi: 10.1109/JBHI.2022.3171663.
Y. Ding et al., "MVFusFra: A Multi-View Dynamic Fusion Framework for Multimodal Brain Tumor Segmentation," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 4, pp. 1570-1581, April 2022, doi: 10.1109/JBHI.2021.3122328.
S. Huda, J. Yearwood, H. F. Jelinek, M. M. Hassan, G. Fortino and M. Buckland, "A Hybrid Feature Selection With Ensemble Classification for Imbalanced Healthcare Data: A Case Study for Brain Tumor Diagnosis," in IEEE Access, vol. 4, pp. 9145-9154, 2016, doi: 10.1109/ACCESS.2016.2647238.
Z. Huang et al., "Convolutional Neural Network Based on Complex Networks for Brain Tumor Image Classification With a Modified Activation Function," in IEEE Access, vol. 8, pp. 89281-89290, 2020, doi: 10.1109/ACCESS.2020.2993618.
A.Kujur, Z. Raza, A. A. Khan and C. Wechtaisong, "Data Complexity Based Evaluation of the Model Dependence of Brain MRI Images for Classification of Brain Tumor and Alzheimer’s Disease," in IEEE Access, vol. 10, pp. 112117-112133, 2022, doi: 10.1109/ACCESS.2022.3216393.
R. Cristin, K. S. Kumar and P. Anbhazhagan, "Severity Level Classification of Brain Tumor based on MRI Images using Fractional-Chicken Swarm Optimization Algorithm," in The Computer Journal, vol. 64, no. 10, pp. 1514-1530, June 2021, doi: 10.1093/comjnl/bxab057.
H. H. Sultan, N. M. Salem and W. Al-Atabany, "Multi-Classification of Brain Tumor Images Using Deep Neural Network," in IEEE Access, vol. 7, pp. 69215-69225, 2019, doi: 10.1109/ACCESS.2019.2919122.
J. J. Corso, E. Sharon, S. Dube, S. El-Saden, U. Sinha and A. Yuille, "Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification," in IEEE Transactions on Medical Imaging, vol. 27, no. 5, pp. 629-640, May 2008, doi: 10.1109/TMI.2007.912817.
S. Mohsen, A. M. Ali, E. -S. M. El-Rabaie, A. ElKaseer, S. G. Scholz and A. M. A. Hassan, "Brain Tumor Classification Using Hybrid Single Image Super-Resolution Technique With ResNext101_32× 8d and VGG19 Pre-Trained Models," in IEEE Access, vol. 11, pp. 55582-55595, 2023, doi: 10.1109/ACCESS.2023.3281529.
Ç. Özkaya and Ş. Sağiroğlu, "Glioma Grade Classification Using CNNs and Segmentation With an Adaptive Approach Using Histogram Features in Brain MRIs," in IEEE Access, vol. 11, pp. 52275-52287, 2023, doi: 10.1109/ACCESS.2023.3273532.
S. Solanki, U. P. Singh, S. S. Chouhan and S. Jain, "Brain Tumor Detection and Classification Using Intelligence Techniques: An Overview," in IEEE Access, vol. 11, pp. 12870-12886, 2023, doi: 10.1109/ACCESS.2023.3242666.
A. Islam, S. M. S. Reza and K. M. Iftekharuddin, "Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors," in IEEE Transactions on Biomedical Engineering, vol. 60, no. 11, pp. 3204-3215, Nov. 2013, doi: 10.1109/TBME.2013.2271383.
A.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," in IEEE Transactions on Medical Imaging, vol. 37, no. 8, pp. 1943-1954, Aug. 2018, doi: 10.1109/TMI.2018.2805821.
G. Rezaei, H. Gholami, M. R. Tootoonchi, and Dehbozorgi, M. H. (2023). Improving the Performance of Gas Extraction by Reducing the Shutdown Time Using an RCA-based Approach–A Case Study. Journal of Advanced Research in Technology and Innovation Management, 4(1), 1–14.
R. Parveen, M. Nabi, F. A. Memon, S. Zaman and M. Ali, “A Review and Survey of Artificial Neural Network in Medical Science”, Journal of Advanced Research in Computing and Applications, Vol. 3, No. 1. pp. 7-16, 2016.
Gopalakrishnan Subburayalu, Hemanand Duraivelu, Arun Prasath Raveendran, Rajesh Arunachalam, Deepika Kongara & Chitra Thangavel. “Cluster based malicious node detection system for mobile Ad-Hoc network using ANFIS classifier”, Journal of Applied Security Research, 2021.
Chitraa. T, Sundara. C, Gopalakrishnan. S, “Investigation and classification of chronic wound tissue images using random forest algorithm (RF)”, no. 1, pp. 643–651, 2022.
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.
