Performance Evaluation of ML Algorithms for Brain Tumor Classification on Brats Dataset

Abstract—Brain neoplasms represent some of the most severe and life-threatening manifestations of cancer, with gliomas constituting the most prevalent subtype, which is further classified into Low-Grade Glioma (LGG) and High-Grade Glioma (HGG). The prompt identification, precise categorization, and accurate delineation of these tumors are imperative for effective diagnostic assessment, prognostic evaluation, and therapeutic strategy formulation. Nevertheless, the manual evaluation of magnetic resonance imaging (MRI) scans is labor-intensive and prone to inter-observer variability. In recent years, advancements in artificial intelligence (AI), particularly within the realms of machine learning (ML) and deep learning (DL) methodologies, have markedly enhanced the automation and precision of brain tumor identification and evaluation.This comprehensive review paper investigates contemporary developments in the automated classification and segmentation of brain tumors utilizing the BraTS dataset, which is widely recognized as a standard reference within the neuroimaging research community. We analyze a diverse array of methodologies, encompassing traditional machine learning algorithms, Convolutional Neural Networks (CNNs), UNet architectures, hybrid models, and ensemble-based strategies. The article underscores techniques that synergistically integrate handcrafted radiomic features with deep learning features to enhance both model robustness and interpretability. Documented accuracy rates in the studies reviewed vary from 66% to 99%, with Dice similarity coefficients employed to assess the segmentation efficacy of tumor sub-regions including tumor core (TC), whole tumor (WT), and enhancing tumor (ET). This scholarly endeavor aspires to furnish researchers and practitioners with valuable insights into cutting-edge methodologies for brain tumor analysis and to offer direction for forthcoming advancements in this domain.

Brain tumor, Glioma, BraTS, Deep learning, Machine learning, MRI, Classification, Segmentation.

1. DeAngelis, Lisa M,” Brain tumors”, New England journal of medicine 344.2 (2001): 114-123. [Google Scholar] [Crossref]

2. Borole, Vipin Y., Sunil S. Nimbhore, and Dr Seema S. Kawthekar,” Image processing techniques for brain tumor detection: A review”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) 4.5 (2015): 2. [Google Scholar] [Crossref]

3. Sharma, Komal, Akwinder Kaur, and Shruti Gujral,” Brain tumor detection based on machine learning algorithms”, International Journal of Computer Applications 103.1 (2014). [Google Scholar] [Crossref]

4. Abdalla, Hussna Elnoor Mohammed, and Mohammed Yagoub Esmail,” Brain tumor detection by using artificial neural network”, 2018 International conference on computer, control, electrical, and electronics engineering (ICCCEEE). IEEE, 2018. [Google Scholar] [Crossref]

5. Amin, Javeria, et al,” A distinctive approach in brain tumor detection and classification using MRI”, Pattern Recognition Letters 139 (2020): 118127. [Google Scholar] [Crossref]

6. Nazir, Maria, Sadia Shakil, and Khurram Khurshid,” Role of deep learning in brain tumor detection and classification (2015 to 2020): A review”, Computerized medical imaging and graphics 91 (2021): 101940. [Google Scholar] [Crossref]

7. Pereira, Sergio, et al,” Brain tumor segmentation using convolutional´ neural networks in MRI images”, IEEE transactions on medical imaging 35.5 (2016): 1240-1251. [Google Scholar] [Crossref]

8. Tiwari, Arti, Shilpa Srivastava, and Millie Pant,” Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019”, Pattern recognition letters 131 (2020): 244-260. [Google Scholar] [Crossref]

9. Anantharajan, Shenbagarajan, Shenbagalakshmi Gunasekaran, and Thavasi Subramanian,” MRI brain tumor detection using deep learning and machine learning approaches”, Measurement: Sensors 31 (2024): 101026. [Google Scholar] [Crossref]

10. Saeedi, Soheila, et al,” MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques”, BMC Medical Informatics and Decision Making 23.1 (2023): 16. [Google Scholar] [Crossref]

11. Soomro, Toufique A., et al,” Image segmentation for MR brain tumor detection using machine learning: a review”, IEEE Reviews in Biomedical Engineering 16 (2022): 70-90. [Google Scholar] [Crossref]

12. Khan, Abdul Hannan, et al,” Intelligent model for brain tumor identification using deep learning”, Applied Computational Intelligence and Soft Computing 2022.1 (2022): 8104054. [Google Scholar] [Crossref]

13. Brindha, P. Gokila, et al,” Brain tumor detection from MRI images using deep learning techniques”, IOP conference series: materials science and engineering. Vol. 1055. No. 1. IOP Publishing, 2021. [Google Scholar] [Crossref]

14. Ali, Saqib, et al,” A comprehensive survey on brain tumor diagnosis using deep learning and emerging hybrid techniques with multi-modal MR image”, Archives of computational methods in engineering 29.7 (2022): 4871-4896. [Google Scholar] [Crossref]

15. Hajmohamad, Abdulsalam, and Hasan Koyuncu, ”3t2FTS: A novel feature transform strategy to classify 3D MRI voxels and its application on HGG/LGG classification”, Machine Learning and Knowledge Extraction 5.2 (2023):359-383. [Google Scholar] [Crossref]

16. Sivakumar, J., S. Rajesh Kannan, and K. Suresh Manic,” Automated classification of brain tumors into LGG/HGG using concatenated deep and handcrafted features”, Frontiers of Artificial Intelligence in Medical Imaging. Bristol, UK: IOP Publishing,2022. 7-1. [Google Scholar] [Crossref]

17. Lefkovits, Szidonia, L´ aszl´ o Lefkovits, and L´ aszl´ o Szil´ agyi,” HGG and´ LGG brain tumor segmentation in multi-modal MRI using pretrained convolutional neural networks of Amazon Sagemaker”, Applied Sciences 12.7 (2022):3620. [Google Scholar] [Crossref]

18. Battalapalli, Dheerendranath, et al,” An optimal brain tumor segmentation algorithm for clinical MRI dataset with low resolution and noncontiguous slices”, BMC Medical Imaging 22.1 (2022): 89. [Google Scholar] [Crossref]

19. Xu, Chenan, et al,” An automated approach for predicting glioma grade and survival of LGG patients using CNN and radiomics”, Frontiers in Oncology 12 (2022): 969907. [Google Scholar] [Crossref]

20. Shomirov, Ahliddin, Jing Zhang, and Mohammad Masum Billah,” Brain tumor segmentation of HGG and LGG MRI images using WFL-based 3D U-net”, Journal of Biomedical Science and Engineering 15.10 (2022): 241-260. [Google Scholar] [Crossref]

21. Maram, BharathSimhaReddy, and Pooja Rana,” Brain tumour detection on brats 2020 using u-net”, 2021 9th International conference on reliability, Infocom technologies and optimization (Trends and future directions) (ICRITO). IEEE, 2021. [Google Scholar] [Crossref]

22. Dequidt, Paul, et al,” Exploring radiologic criteria for glioma grade classification on the BraTS dataset”, IRBM 42.6 (2021): 407-414. [Google Scholar] [Crossref]

23. Choi, Sang-Geun, and Chae-Bong Sohn,” Detection of HGG and LGG brain tumors using U-Net”, Medico-legal Update 19.1 (2019): 560-565. [Google Scholar] [Crossref]

24. Polly, Farzana Parvin, et al,” Detection and classification of HGG and LGG brain tumor using machine learning”, 2018 International Conference on Information Networking (ICOIN). IEEE, 2018. [Google Scholar] [Crossref]

• Most Advanced Construction Material for Typical Boilers
• Nutrigenetic Impact of PEMT Gene Polymorphism Rs7946 On Choline Metabolism and Its Role in Personalised Nutrition
• Evaluating the Influence Factors of Media on Investor Sentiment Towards Stock Market Investments.
• To Compare the Effectiveness of Manual Traction and Mechanical Traction in Patient with Cervicogenic Headache
• Is Green Human Resource Management the Newest Trend or A Strategic Necessity?