The growth of cells in the brain or nearby tissues, known as brain tumors, they may be termed benign(non-cancerous) or malignant(cancerous) and can cause various symptoms depending on their location and size. Brain tumor, both benign and malignant cause significant clinical challenges due to their complexity and the diverse range of symptoms they produce depending on their location, size, and type. Tumor classification has traditionally relied on histopathological examination, but molecular insights are becoming crucial in improving accuracy and treatment strategies. Clustering techniques particularly DBSCAN (Density-Based Spatial Clustering of Applications with Noise) identify molecular subtypes in brain tumor datasets, specifically genetic expression data from the GSE50161 dataset. The goal is to improve the detection of Brain Tumor patterns ultimately contributing to better diagnostic, prognostic, and treatment strategies for the patients. Identifying distinct molecular subtypes through genetic expression data can assist in creating personalized treatment plans for patients. By categorizing tumors more accurately, clinicians can choose therapies that target specific molecular mechanisms leading to better outcome Ultimately leading to enhanced accuracy in brain tumor detection.
| Published in | International Journal of Wireless Communications and Mobile Computing (Volume 12, Issue 1) |
| DOI | 10.11648/j.wcmc.20251201.12 |
| Page(s) | 16-22 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Brain Tumors, Bioinformatics, Gene Expression, Clustering, DBSCAN, Model Predictions
| [1] | Amin J., Sharif M., Raza M., Saba T., Anjum M. A. Brain tumor detection using statistical and machine learning method. Comput. Methods Programs Biomed. 2019; 177: 69–79. |
| [2] | Khan A. H., Abbas S., Khan M. A., Farooq U., Khan W. A., Siddiqui S. Y., Ahmad A. Intelligent model for brain tumor identification using deep learning. Appl. Comput. Intell. Soft Comput. 2022; 2022: 8104054. |
| [3] | Abdel-Gawad AH, Lobna A, Ahmed S, Radwan G (2020) Optimized Edge Detection Technique for Brain Tumor Detection in MR Images. IEEE Access 8: 136243–136259. |
| [4] | B. R. Reddy, Y. Vijay Kumar and M. Prabhakar, "Clustering large amounts of healthcare datasets using fuzzy c-means algorithm," 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), Coimbatore, India, 2019, pp. 93-97. |
| [5] | X. Li, Y. Kang, Y. Zhu, G. Zheng and J. Wang, "An improved medical image segmentation algorithm based on clustering techniques," 2017 10th International Congress on Image and Signal Processing, Biomedical Engineering and Informatics (CISP-BMEI), Shanghai, 2017, pp. 1-5. |
| [6] | Budati A. K., Katta R. B. An automated brain tumor detection and classification from MRI images using machine learning technique s with IoT. Environ. Dev. Sustain. 2022; 24: 10570–10584. |
| [7] | Murtagh, Fionn& Contreras, Pedro. (2017). Algorithms for hierarchical clustering: An overview, II. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 7. |
| [8] | Cilia ND, De Stefano C, Fontanella F, Raimondo S, Scotto di Freca A. An Experimental Comparison of Feature-Selection and Classification Methods for Microarray Datasets. Information. 2019; 10(3): 109. |
| [9] | M. M. Mufassirin, R. G. Ragel, A novel filter-wrapper based feature selection approach for cancer data classification, 2018 IEEE International Conference on Information and Automation for Sustainability (ICIAfS), IEEE, 2018, pp. 1-6. |
| [10] | Huyen Nguyen, T. T., Van, P. N., Tran, Q., XuanVo, N., & Quang Vo, T. (2020). Cancer classification from microarray data for genomic disorder research using optimal discriminant independent component analysis and kernel extreme learning machine. International Journal for Numerical Methods in Biomedical Engineering, 36. |
| [11] | Saradhi, A. V. (2018). An Efficient Method K Detection of Tumour Volume in Brain MRI Scans. |
| [12] | M M, P S. MRI Brain Tumour Segmentation Using Hybrid Clustering and Classification by Back Propagation Algorithm. Asian Pac J Cancer Prev. 2018 Nov 29; 19(11): 3257-3263. |
| [13] | Kumar, D. M., Satyanarayana, D. & Prasad, M. N. G. MRI brain tumor detection using optimal possibilistic fuzzy C-means clustering algorithm and adaptive k-nearest neighbor classifier. J Ambient Intell Human Comput 12, 2867–2880 (2021). |
| [14] | Zhang, S., Zeng, T., Hu, B., Zhang, Y. H., Feng, K., Chen, L., et al. (2020). Discriminating Origin Tissues of Tumor Cell Lines by Methylation Signatures and Dys-Methylated Rules. Front. Bioeng. Biotechnol. 8, 507. |
APA Style
Tezaaw, Y., Lakshmi, K. V. (2025). Detection of Brain Tumors Using Optimized Features in Clustering Techniques for Enhanced Model Development with Accuracy. International Journal of Wireless Communications and Mobile Computing, 12(1), 16-22. https://doi.org/10.11648/j.wcmc.20251201.12
ACS Style
Tezaaw, Y.; Lakshmi, K. V. Detection of Brain Tumors Using Optimized Features in Clustering Techniques for Enhanced Model Development with Accuracy. Int. J. Wirel. Commun. Mobile Comput. 2025, 12(1), 16-22. doi: 10.11648/j.wcmc.20251201.12
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Download@article{10.11648/j.wcmc.20251201.12,
author = {Yavanaboina Tezaaw and Kumba Vijaya Lakshmi},
title = {Detection of Brain Tumors Using Optimized Features in Clustering Techniques for Enhanced Model Development with Accuracy
},
journal = {International Journal of Wireless Communications and Mobile Computing},
volume = {12},
number = {1},
pages = {16-22},
doi = {10.11648/j.wcmc.20251201.12},
url = {https://doi.org/10.11648/j.wcmc.20251201.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20251201.12},
abstract = {The growth of cells in the brain or nearby tissues, known as brain tumors, they may be termed benign(non-cancerous) or malignant(cancerous) and can cause various symptoms depending on their location and size. Brain tumor, both benign and malignant cause significant clinical challenges due to their complexity and the diverse range of symptoms they produce depending on their location, size, and type. Tumor classification has traditionally relied on histopathological examination, but molecular insights are becoming crucial in improving accuracy and treatment strategies. Clustering techniques particularly DBSCAN (Density-Based Spatial Clustering of Applications with Noise) identify molecular subtypes in brain tumor datasets, specifically genetic expression data from the GSE50161 dataset. The goal is to improve the detection of Brain Tumor patterns ultimately contributing to better diagnostic, prognostic, and treatment strategies for the patients. Identifying distinct molecular subtypes through genetic expression data can assist in creating personalized treatment plans for patients. By categorizing tumors more accurately, clinicians can choose therapies that target specific molecular mechanisms leading to better outcome Ultimately leading to enhanced accuracy in brain tumor detection.
},
year = {2025}
}
TY - JOUR T1 - Detection of Brain Tumors Using Optimized Features in Clustering Techniques for Enhanced Model Development with Accuracy AU - Yavanaboina Tezaaw AU - Kumba Vijaya Lakshmi Y1 - 2025/03/21 PY - 2025 N1 - https://doi.org/10.11648/j.wcmc.20251201.12 DO - 10.11648/j.wcmc.20251201.12 T2 - International Journal of Wireless Communications and Mobile Computing JF - International Journal of Wireless Communications and Mobile Computing JO - International Journal of Wireless Communications and Mobile Computing SP - 16 EP - 22 PB - Science Publishing Group SN - 2330-1015 UR - https://doi.org/10.11648/j.wcmc.20251201.12 AB - The growth of cells in the brain or nearby tissues, known as brain tumors, they may be termed benign(non-cancerous) or malignant(cancerous) and can cause various symptoms depending on their location and size. Brain tumor, both benign and malignant cause significant clinical challenges due to their complexity and the diverse range of symptoms they produce depending on their location, size, and type. Tumor classification has traditionally relied on histopathological examination, but molecular insights are becoming crucial in improving accuracy and treatment strategies. Clustering techniques particularly DBSCAN (Density-Based Spatial Clustering of Applications with Noise) identify molecular subtypes in brain tumor datasets, specifically genetic expression data from the GSE50161 dataset. The goal is to improve the detection of Brain Tumor patterns ultimately contributing to better diagnostic, prognostic, and treatment strategies for the patients. Identifying distinct molecular subtypes through genetic expression data can assist in creating personalized treatment plans for patients. By categorizing tumors more accurately, clinicians can choose therapies that target specific molecular mechanisms leading to better outcome Ultimately leading to enhanced accuracy in brain tumor detection. VL - 12 IS - 1 ER -
Department of Computer Science, Sri Venkateshwara University, Tirupati, India
Department of Computer Science, Sri Venkateshwara University, Tirupati, India
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