Advancing Brain Tumor Detection: Optimized Machine Learning Models for Enhanced Diagnostic Accuracy

D Shobana; V Vijayalakshmi; Mariya Princy Antony Saviour; K Makanyadevi; Kalaimagal Sivamuni; Veeraiyah Thangasamy

doi:10.57159/jcmm.5.1.25265

Authors

D Shobana Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu, India 602105
V Vijayalakshmi Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India 600062
Mariya Princy Antony Saviour Department of Electronics and Communication Engineering, St. Joseph University, Chennai, Tamil Nadu, India 600123
K Makanyadevi Department of Computer Science and Engineering, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, India 639113
Kalaimagal Sivamuni Department of AI & DS, Panimalar Engineering College, Varadharajapuram, Poonamallee, Chennai, Tamil Nadu, India 600123
Veeraiyah Thangasamy Department of Electronics and Communication Engineering, V.S.B. Engineering College, Karur, Tamil Nadu, India 639111

DOI:

https://doi.org/10.57159/jcmm.5.1.25265

Keywords:

Brain Tumor, Cancer Diagnosis, Deep Neural Network, Medical Imaging, Cognitive Function

Abstract

Segmentation of brain tumors from MRI continues to be difficult because tumors are different and there are not enough of each type. This implementation study improves Mask R-CNN for BraTS2020 by using three new ideas: a ResNet101 backbone that was trained on RSNA pneumonia data (Adam lr=0.001, batch=2 on RTX 3060), MRI-specific augmentation (57,195 2D slices from 369 3D volumes), and one-class loss weighting (λmask = 2.0) tuned to a 9:1 background tumor-pixel ratio to manage the imbalance in BraTS2020. This approach improved recall by 6 points compared with λmask = 1.0. With λmask = 1.0, the recall value is 0.66, and for λmask = 2.0, the recall value is increased to 0.72. Five-fold cross-validation shows that the results are stable (Dice=0.75, p < 0.01 vs ImageNet baseline), with performance by region: core=0.72, edema=0.68, and enhancing=0.76, and probability calibration characterized by an Expected Calibration Error (ECE) of 0.82 under a coarse, three-bin reliability analysis. To balance high-sensitivity tumor detection with a recall of 0.72 on the BraTS2020 dataset, our proposed method considered resource constraints for real-time deployment at 15 FPS.

References

E. Schulz and S. J. Gershman, “The algorithmic architecture of exploration in the human brain,” Current Opinion in Neurobiology, vol. 55, pp. 7–14, 2019.

A. Del Dosso, J.-P. Urenda, T. Nguyen, and G. Quadrato, “Upgrading the physiological relevance of human brain organoids,” Neuron, vol. 107, no. 6, pp. 1014–1028, 2020.

P. J. C. van Lonkhuizen, K. M. Klaver, J. S. Wefel, M. M. Sitskoorn, S. B. Schagen, and K. Gehring, “Interventions for cognitive problems in adults with brain cancer: A narrative review,” European Journal of Cancer Care, vol. 28, no. 3, p. e13088, 2019.

S. L. Fernandes, U. J. Tanik, V. Rajinikanth, and K. A. Karthik, “A reliable framework for accurate brain image examination and treatment planning based on early diagnosis support for clinicians,” Neural Computing and Applications, vol. 32, no. 20, pp. 15897–15908, 2020.

Z. U. Rehman, S. S. Naqvi, T. M. Khan, M. A. Khan, and T. Bashir, “Fully automated multi-parametric brain tumour segmentation using superpixel based classification,” Expert Systems with Applications, vol. 118, pp. 598–613, 2019.

Z. U. Rehman, M. S. Zia, G. R. Bojja, M. Yaqub, F. Jinchao, and K. Arshid, “Texture based localization of a brain tumor from MR-images by using a machine learning approach,” Medical Hypotheses, vol. 141, p. 109705, 2020.

C. K. V. and G. R. G. King, “Brain tumour classification: A comprehensive systematic review on various constraints,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 11, no. 3, pp. 1–13, 2023.

K. Rezaei, H. Agahi, and A. Mahmoodzadeh, “Multi-objective differential evolution-based ensemble method for brain tumour diagnosis,” IET Image Processing, vol. 13, no. 9, pp. 1421–1430, 2019.

R. Ezhilarasi and P. Varalakshmi, “Tumor detection in the brain using faster R-CNN,” in Proceedings of the 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 388–392, 2018.

H. Mohsen, E.-S. A. El-Dahshan, E.-S. M. El-Horbaty, and A.-B. M. Salem, “Classification using deep learning neural networks for brain tumors,” Future Computing and Informatics Journal, vol. 3, no. 1, pp. 68–71, 2018.

M. Siar and M. Teshnehlab, “Brain tumor detection using deep neural network and machine learning algorithm,” in Proceedings of the International Conference on Computer and Knowledge Engineering (ICCKE), pp. 363–368, 2019.

C. L. Choudhury, C. Mahanty, R. Kumar, and B. K. Mishra, “Brain tumor detection and classification using convolutional neural network and deep neural network,” in Proceedings of the International Conference on Computer Science, Engineering and Applications (ICCSEA), pp. 1–6, 2020.

M. A. Naser and M. J. Deen, “Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images,” Computers in Biology and Medicine, vol. 121, p. 103758, 2020.

M. K. Islam, M. S. Ali, M. S. Miah, M. M. Rahman, M. S. Alam, and M. A. Hossain, “Brain tumor detection in MR image using superpixels, principal component analysis and template based k-means clustering algorithm,” Machine Learning with Applications, vol. 5, p. 100044, 2021.

T. A. Jemimma and Y. J. Vetharaj, “Watershed algorithm based dapp features for brain tumor segmentation and classification,” in Proceedings of the International Conference on Soft Computing Systems and Intelligent Technologies (ICSSIT), pp. 155–160, 2018.

G. Hemanth, M. Janardhan, and L. Sujihelen, “Design and implementing brain tumor detection using machine learning approach,” in Proceedings of the International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1289–1293, 2019.

S. K. Chandra and M. K. Bajpai, “Effective algorithm for benign brain tumor detection using fractional calculus,” in Proceedings of the IEEE Region 10 Conference (TENCON), pp. 2408–2412, 2018.

M. Gurbină, M. Lascu, and D. Lascu, “Tumor detection and classification of MRI brain image using different wavelet transforms and support vector machines,” in Proceedings of the International Symposium on Signals, Circuits and Systems (ISSCS) / Telecommunications and Signal Processing (TSP), pp. 505–509, 2019.

C. Sheela and G. Suganthi, “Brain tumor segmentation with radius contraction and expansion based initial contour detection for active contour model,” Multimedia Tools and Applications, vol. 79, no. 33–34, pp. 23793–23810, 2020.

V. R. Kasu, B. K. K. Malamuthu, B. S. Kumar, V. S. Pandi, E. Sivajothi, and D. S. Deepika, “Implementing machine learning for AI-powered solutions in robotics, computer vision, and natural language processing,” in Proceedings of the Global Conference in Emerging Technology (GINOTECH), (Pune, India), pp. 1–6, 2025.

A. Wadhwa, A. Bhardwaj, and V. S. Verma, “A review on brain tumor segmentation of MRI images,” Magnetic Resonance Imaging, vol. 61, pp. 247–259, 2019.

M. A. Asok, V. Samuthira Pandi, N. Yuvaraj, S. Supriya, A. S. K. Joseph, and T. M. Thiyagu, “Employing artificial intelligence and machine learning to create adaptive models for improved predictive accuracy in dynamical real-world applications,” in Proceedings of the 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI), pp. 1172–1177, 2025.

J. Amin, M. Sharif, M. Yasmin, and S. L. Fernandes, “A distinctive approach in brain tumor detection and classification using MRI,” Pattern Recognition Letters, vol. 139, pp. 118–127, 2020.

H. A. Khalil, S. Darwish, Y. M. Ibrahim, and O. F. Hassan, “3D-MRI brain tumor detection model using modified version of level set segmentation based on dragonfly algorithm,” Symmetry, vol. 12, no. 8, p. 1256, 2020.

CBICA, Perelman School of Medicine, University of Pennsylvania, “Brain tumor segmentation (brats) challenge 2020: Scope.”