A New Approach to Brain Tumor Detection with CNNS: Addressing the Issues of Standardization and Generalizability
Keywords:
brain tumor detection, CNNs, standardization, generalizability, preprocessing techniquesAbstract
Brain tumour detection is a key task in medical imaging that necessitates precise and dependable approaches for early detection and treatment. Among imaging modalities, MRI is the gold standard for spotting malignant growths in the brain. Brain tumour size, shape, and location can all be discerned from MRI scans of the brain. Brian tumour detection can be done with visual analysis, medical image processing or computer aided detection. The motivation for this study is the current lack of universally applicable methods for detecting brain tumours. The lack of standardisation in brain images is a major challenge for current CNN models, typically resulting in subpar performance and poor generalizability. As a result, the goal of this research is to establish a procedure that will help standardise and broaden the applicability of CNN-based brain tumour detection of specific type. This research aims to improve generalizability by utilising a CNN models on large-scale datasets, and increase standardisation in brain images by incorporating robust preprocessing techniques, such as standardisation, feature extraction,segmentation etc. To test the performance of our proposed method with several deep learning techniques, including support vector machine (SVM) and random forest algorithm, we accomplished extensive experiments on an enormous data set comprising of brain scans from a broad range of sources. The outcomes show substantial gains in precision and generalizability over the current gold standard. The overall classification accuracy of CNN algorithm for barin tumor detection is 98.28%.
Downloads
References
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., & Maier-Hein, K. H. (2017). Brain tumor segmentation and radiomics survival prediction: Contribution to the BRATS 2017 challenge. arXiv preprint arXiv:1802.10508.
Deepak, S., & Ameer, P. M. (2021). Automated categorization of brain tumor from MRI using CNN features and SVM. Journal of Ambient Intelligence and Humanized Computing, 12(5), 1-13.
Zhang, Y., Liu, J., Chen, W., Yang, Y., & Li, Y. (2018). A multi-modal deep learning approach for brain tumor detection. arXiv preprint arXiv:1803.07888.
Alfonse, M., & Salem, A. M. (2016). An Automatic Classification of Brain Tumors through MRI Using Support Vector Machine. Egyptian Computer Science Journal , Volume(40), 1-11.
Articles with DOI
Jia, Z., & Chen, D. (2020). Brain tumor identification and classification of MRI images using deep learning techniques. IEEE Access, 8, 3016319. doi:10.1109/ACCESS.2020.3016319
Abd-Ellah, Mahmoud et al. "A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned." Magnetic Resonance Imaging. 10.1016/j.mri.2019.05.028.
M. Arabahmadi, R. Farahbakhsh, and J. Rezazadeh, "Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging," Sensors, vol. 22, no. 5, p. 1960, Mar. 2022, doi: 10.3390/s22051960.
Rammurthy, D., & Mahesh, P. K. (2022). Whale Harris hawks optimization based deep learning classifier for brain tumor detection using MRI images. Journal of King Saud University - Computer and Information Sciences, 34(6), Part B, 3259-3272. doi:10.1016/j.jksuci.2020.08.006
E. Sanjay and P. Swarnalatha (2022), "A Survey on Various Machine Learning Techniques for an Efficient Brain Tumor Detection from MRI Images," IJEER 10(2), 177-182. DOI: 10.37391/IJEER.100222.
Khan, M. A., Ashraf, I., Alhaisoni, M., Damaševičius, R., Scherer, R., Rehman, A., & Chan Bukhari, S. A. (2020). Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists. Diagnostics, 10(8), 565. https://doi.org/10.3390/diagnostics10080565
Anitha, V., & Murugavalli, S. (2016). Brain tumour classification using two‐tier classifier with adaptive segmentation technique. Iet Computer Vision, 10(1), 9–17. https://doi.org/10.1049/iet-cvi.2014.0193
Luo, W., Li, Y., Urtasun, R. et al. (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35, 18–31 .
Liu, Z., Tong, L., Chen, L. et al. (2023) Deep learning based brain tumor segmentation: a survey. Complex Intell. Syst. 9, 1001–1026 (2023). https://doi.org/10.1007/s40747-022-00815-5
Alam MS, Rahman MM, Hossain MA, Islam MK, Ahmed KM, Ahmed KT, Singh BC, Miah MS. *(2019)Automatic Human Brain Tumor Detection in MRI Image Using Template-Based K Means and Improved Fuzzy C Means Clustering Algorithm. Big Data and Cognitive Computing. 2019; 3(2):27. https://doi.org/10.3390/bdcc3020027
Revanth Kumar, P., Katti, A., Nandan Mohanty, S., & Nath Senapati, S. (2022). A Deep Learning-Based Approach for an Automated Brain Tumor Segmentation in MR Images. In Lecture Notes in Electrical Engineering (Vol. 888, pp. 87–97). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-1520-8_7
Xie, Y., Zaccagna, F., Rundo, L., Testa, C., Agati, R., Lodi, R., Manners, D. N., & Tonon, C. (2022). Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives. Diagnostics (Basel, Switzerland), 12(8), 1850. https://doi.org/10.3390/diagnostics12081850
Buchlak, Q. D., Esmaili, N., Leveque, J. C., Bennett, C., Farrokhi, F., & Piccardi, M. (2021). Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review. Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia, 89, 177–198. https://doi.org/10.1016/j.jocn.2021.04.043
Qingji Tian, Yongtang Wu, Xiaojun Ren, Navid Razmjooy,(2021) A New optimized sequential method for lung tumor diagnosis based on deep learning and converged search and rescue algorithm,Biomedical Signal Processing and Control,Volume 68,2021,102761,ISSN 1746-8094,https://doi.org/10.1016/j.bspc.2021.102761.
Wu, P., & Shen, J. (2021). Brain tumor diagnosis based on convolutional neural network improved by a new version of political optimizer. Biomedical Signal Processing and Control, 68, 102761. https://doi.org/10.1016/j.bspc.2021.102761
Thayumanavan, M., & Ramasamy, A. (2021). An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier. Concurrent Engineering Research and Applications, 29(3), 266–274. https://doi.org/10.1177/1063293X211010542
Abdul Hannan Khan, Sagheer Abbas, Muhammad Adnan Khan, Umer Farooq, Wasim Ahmad Khan, Shahan Yamin Siddiqui, Aiesha Ahmad. (2022). Intelligent Model for Brain Tumor Identification Using Deep Learning. Applied Computational Intelligence and Soft Computing, 2022, Article ID 8104054, 10 pages. https://doi.org/10.1155/2022/8104054
Alharan, A. F. H., Fatlawi, H. K., & Ali, N. S. (2019, June 1). A cluster-based feature selection method for image texture classification. Indonesian Journal of Electrical Engineering and Computer Science, 14(3), 1433. https://doi.org/10.11591/ijeecs.v14.i3.pp1433-1442
Anantharajan, S., & Gunasekaran, S. (2021). Automated brain tumor detection and classification using weighted fuzzy clustering algorithm, deep auto encoder with barnacle mating algorithm and random forest classifier techniques. International Journal of Imaging Systems and Technology, 31, 1970-1988. https://doi.org/10.1002/ima.22582
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.