An Ensemble Approach for Comprehensive Brain Tumour Detection Using MRI-Based Machine Learning Models


  • Kavita Jain, Deepali R. Vora,Teena Varma, Harshali Patil, Adit Anil Deshmukh, Asad Shaikh, John Baby, Shivam Goswami


BraTS 2021, Brain Tumour, Br35H, CNN, Machine Learning, MRI (Magnetic Resonance Imaging), ResNet


In the realm of medical imaging, the rapid evolution of techniques and exponential growth of data have emphasised the significance of automatic and reliable tools for brain tumour detection. This project proposes a system designed to detect brain tumours utilising Magnetic Resonance Imaging (MRI) data. Two distinct models leveraging advanced machine learning algorithms, particularly Convolutional Neural Networks (CNNs), are developed using multiple datasets. The BraTS dataset is used for segmentation tasks, providing detailed information about specific brain tumour regions. Concurrently, the Br35H dataset is used for binary classification, distinguishing the presence or absence of tumours. Furthermore, the brain tumour dataset from Kaggle adds another dimension to this study, offering diverse data samples. The proposed system encompasses a two-step approach. First, a segmentation model is fine-tuned on the BraTS dataset to identify specific regions within brain scans. Subsequently, a classification model is trained using both the Br35H and Kaggle datasets. Ensemble learning techniques, involving an ensemble of CNN architectures such as ResNet and VGG, along with the exploration of ensemble methods like AdaBoost, are employed for effective classification.


Download data is not yet available.


Yashwant Kurmi and Vijayshri Chaurasia. “Classification of magnetic resonance images for brain tumour detection”. In: IET Image Processing 14.12 (2020), pp. 2808–2818

Chattopadhyay, Arkapravo, and Mausumi Maitra. "MRI-based brain tumour image detection using CNN based deep learning method." Neuroscience informatics 2.4 (2022): 100060.

G. Hemanth, M. Janardhan and L. Sujihelen, "Design and Implementing Brain Tumor Detection Using Machine Learning Approach," 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2019, pp. 1289-1294, doi: 10.1109/ICOEI.2019.8862553

V Divya Dharshini et al. “Brain Tumor Detection Using Image Processing Technique from MRI Images Based on OTSU Algorithm”. In: Central Asian Journal of Theoretical and Applied Science 3.5 (2022), pp. 45–66.

Mahmud, Md Ishtyaq, Muntasir Mamun, and Ahmed Abdelgawad. "A deep analysis of brain tumor detection from mri images using deep learning networks." Algorithms 16.4 (2023): 176.

Ranjbarzadeh, Ramin, et al. "Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images." Scientific Reports 11.1 (2021): 10930.

Abdullah Al Nasim, M. D., et al. "Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis." arXiv e-prints (2022): arXiv-2210.

Muyiwa Babayomi, Oluwatosin Atinuke Olagbaju, and Abdulrasheed Adedolapo Kadiri.“Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection”. In: arXiv preprint arXiv:2301.02317 (2023)

Alkassar, Sinan, Mohammed AM Abdullah, and Bilal A. Jebur. "Automatic brain tumour segmentation using fully convolution network and transfer learning." 2019 2nd international conference on electrical, communication, computer, power and control engineering (ICECCPCE). IEEE, 2019.

Sravanthi, N. S. R. D. N., et al. "Brain tumor detection using image processing." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 7.3 (2021): 348-352.

Geethanjali, N., et al. "Brain Tumor Detection and Classification Using Deep Learning." 2023 Winter Summit on Smart Computing and Networks (WiSSCoN). IEEE, 2023.

Çinar, Ahmet, and Muhammed Yildirim. "Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture." Medical hypotheses 139 (2020): 109684.

Tonmoy Hossain et al. “Brain tumor detection using convolutional neural network”. In:2019 1st international conference on advances in science, engineering and robotics technology (ICASERT). IEEE. 2019, pp. 1–6

Menachery, Joel, Ishan Kumar Anand, and Michael Moses Thiruthuvanathan. "Brain Tumor Detectin Using Deep Learning Model." 2023 IEEE International Conference on Contemporary Computing and Communications (InC4). Vol. 1. IEEE, 2023.

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.

Bhuiya, Nabila. A review on the occurrence of brain tumor in adults and pediatrics and the associated risk factors. Diss. Brac University, 2023.

Xie, Quin, et al. "Deep learning for image analysis: Personalizing medicine closer to the point of care." Critical Reviews in Clinical Laboratory Sciences 56.1 (2019): 61-73.

Roy, Sudipta, et al. "Heterogeneity of human brain tumor with lesion identification, localization, and analysis from MRI." Informatics in Medicine Unlocked 13 (2018): 139-150.

Islam, Md Khairul, et al. "Brain tumor detection in MR image using superpixels, principal component analysis and template based K-means clustering algorithm." Machine Learning with Applications 5 (2021): 100044.

Mahadevkar, Supriya V., et al. "A review on machine learning styles in computer vision—techniques and future directions." Ieee Access 10 (2022): 107293-107329.

Wadhwa, Anjali, Anuj Bhardwaj, and Vivek Singh Verma. "A review on brain tumor segmentation of MRI images." Magnetic resonance imaging 61 (2019): 247-259.




How to Cite

Asad Shaikh, John Baby, Shivam Goswami, K. J. D. R. V. V. H. P. A. A. D. . (2024). An Ensemble Approach for Comprehensive Brain Tumour Detection Using MRI-Based Machine Learning Models. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 360–366. Retrieved from



Research Article