Benchmarking Deep Convolutional Neural Networks for Brain Tumor Detection Using Magnetic Resonance Imaging Data
Keywords:
Brain Tumor Classification, Magnetic Resonance Imaging, CNNs, Deep Learning, Medical Image AnalysisAbstract
Brain tumors are associated with high mortality and severe neurological effects, making accurate and timely diagnosis essential. However, manual interpretation of Magnetic Resonance Imaging (MRI) remains subjective and time-consuming. Deep learning, particularly Convolutional Neural Networks (CNNs), offers a promising approach to improving diagnostic accuracy and efficiency. This research systematically benchmarks five CNN architectures (VGG19, DenseNet201, ResNet50, Inception-v3, and MobileNet) on balanced and naturally imbalanced MRI datasets. Its novelty lies in evaluating all models under a unified preprocessing and transferlearning framework across both dataset distributions, with Grad-CAM used to support interpretability. Preprocessing includes resizing, adaptive masking, Gaussian blur, Contrast Limited Adaptive Histogram Equalization (CLAHE), normalization, and augmentation, followed by transfer learning using ImageNet-pretrained models. Performance is evaluated using accuracy, precision, recall, specificity, F1-score, and Area Under the Curve (AUC). Results show that VGG19 achieves the highest accuracy (0.937 for balanced and 0.962 for imbalanced data), recall (0.991 and 0.984, respectively), and F1-score, demonstrating strong sensitivity and generalization. DenseNet201 achieves the highest AUC (0.992 and 0.996) and high specificity, indicating strong overall discriminative performance. These findings suggest that VGG19 is particularly suitable for sensitivity-oriented early screening, whereas DenseNet201 is advantageous when overall discrimination and specificity are prioritized. In conclusion, CNN-based models show substantial potential for brain tumor classification, with architecture selection depending on specific clinical priorities.
References
[1] B. V. S. Rao, R. Kondaveti, R. V. V. S. V. Prasad, V. S. Rao, K. B. S. Sastry, and B. Dasaradharam, “Classification of brain tumours using artificial neural networks,” ACTA IMEKO, vol. 11, no. 1, pp. 1–7, 2022.
[2] K. Noll, A. L. King, L. Dirven, T. S. Armstrong, M. J. B. Taphoorn, and J. S. Wefel, “Neurocognition and health-related quality of life among patients with brain tumors,” Hematology/Oncology Clinics, vol. 36, no. 1, pp. 269–282, 2022.
[3] D. P. R. Chieffo, F. Lino, D. Ferrarese, D. Belella, G. M. Della Pepa, and F. Doglietto, “Brain tumor at diagnosis: From cognition and behavior to quality of life,” Diagnostics, vol. 13, no. 3, pp. 1–12, 2023.
[4] S. M. Vijithananda et al., “Feature extraction from mri adc images for brain tumor classification using machine learning techniques,” Biomedical Engineering Online, vol. 21, pp. 1–21, 2022.
[5] S. Lin, K. Li, and L. Qi, “Cancer stem cells in brain tumors: From origin to clinical implications,” MedComm, vol. 4, no. 4, pp. 1–27, 2023.
[6] J. Latowska, A. Grabowska, Z. Zarebska, K. Kuczynski, B. Kuczynska, and K. Rolle, “Non-coding RNAs in brain tumors, the contribution of lncRNAs, circRNAs, and snoRNAs to cancer development–Their diagnostic and therapeutic potential,” International Journal of Molecular Sciences, vol. 21, no. 19, pp. 1–30, 2020.
[7] F. Yamasaki, “Adolescent and young adult brain tumors: Current topics and review,” International Journal of Clinical Oncology, vol. 27, pp. 457–464, 2022.
[8] Y. Fan et al., “Burden and trends of brain and central nervous system cancer from 1990 to 2019 at the global, regional, and country levels,” Archives of Public Health, vol. 80, pp. 1–14, 2022.
[9] T. A. Soomro et al., “Image segmentation for MR brain tumor detection using machine learning: A review,” IEEE Reviews in Biomedical Engineering, vol. 16, pp. 70–90, 2022.
[10] S. Asif, W. Yi, Q. U. Ain, J. Hou, T. Yi, and J. Si, “Improving effectiveness of different deep transfer learning-based models for detecting brain tumors from MR images,” IEEE Access, vol. 10, pp. 34 716–34 730, 2022.
[11] M. S. I. Khan et al., “Accurate brain tumor detection using deep convolutional neural network,” Computational and Structural Biotechnology Journal, vol. 20, pp. 4733–4745, 2022.
[12] N. Noreen, S. Palaniappan, A. Qayyum, I. Ahmad, and M. O. Alassafi, “Brain tumor classification based on fine-tuned models and the ensemble method,” Computers, Materials and Continua, vol. 67, no. 3, pp. 3967–3982, 2021.
[13] A. M. D. Simo, A. T. Kouanou, V. Monthe, M. K. Nana, and B. M. Lonla, “Introducing a deep learning method for brain tumor classification using MRI data towards better performance,” Informatics in Medicine Unlocked, vol. 44, pp. 1–24, 2024.
[14] H. Kibriya, R. Amin, J. Kim, M. Nawaz, and R. Gantassi, “A novel approach for brain tumor classification using an ensemble of deep and hand-crafted features,” Sensors, vol. 23, no. 10, pp. 1–16, 2023.
[15] M. Agarwal, G. Rani, A. Kumar, P. Kumar, R. Manikandan, and A. H. Gandomi, “Deep learning for enhanced brain tumor detection and classification,” Results in Engineering, vol. 22, pp. 1–12, 2024.
[16] L. R. Carrete, J. S. Young, and S. Cha, “Advanced imaging techniques for newly diagnosed and recurrent gliomas,” Frontiers in Neuroscience, vol. 16, pp. 1–22, 2022.
[17] S. Gassenmaier et al., “Deep learning applications in magnetic resonance imaging: Has the future become present?” Diagnostics, vol. 11, no. 12, pp. 1–11, 2021.
[18] M. I. Mahmud, M. Mamun, and A. Abdelgawad, “A deep analysis of brain tumor detection from MR images using deep learning networks,” Algorithms, vol. 16, no. 4, pp. 1–19, 2023.
[19] M. W. Nadeem, M. A. A. Ghamdi, M. Hussain, M. A. Khan, K. M. Khan, S. H. Almotiri, and S. A. Butt, “Brain tumor analysis empowered with deep learning: A review, taxonomy, and future challenges,” Brain sciences, vol. 10, no. 2, pp. 1–33, 2020.
[20] M. Aamir et al., “A deep learning approach for brain tumor classification using MRI images,” Computers and Electrical Engineering, vol. 101, 2022.
[21] D. A. Wood et al., “Deep learning models for triaging hospital head MRI examinations,” Medical Image Analysis, vol. 78, pp. 1–22, 2022.
[22] R. Zaitoon and H. Syed, “Enhanced brain tumor detection and classification in MRI scans using convolutional neural networks,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 9, pp. 266–275, 2023.
[23] E. Albalawi et al., “Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach,” Frontiers in Computational Neuroscience, vol. 18, pp. 1–19, 2024.
[24] S. Deepak and P. M. Ameer, “Brain tumor categorization from imbalanced MRI dataset using weighted loss and deep feature fusion,” Neurocomputing, vol. 520, pp. 94–102, 2023.
[25] T. Agrawal, P. Choudhary, V. Kumar, P. Singh, M. Diwakar, and S. Kumar, “A comparative study of brain tumor classification on unbalanced dataset using deep neural networks,” Biomedical Signal Processing and Control, vol. 94, 2024.
[26] K. Gasmi et al., “Enhanced brain tumor diagnosis using combined deep learning models and weight selection technique,” Frontiers in Neuroinformatics, vol. 18, pp. 1–14, 2024.
[27] P. M. S. Raja and A. V. Rani, “Brain tumor classification using a hybrid deep autoencoder with bayesian fuzzy clustering-based segmentation approach,” Biocybernetics and Biomedical Engineering, vol. 40, no. 1, pp. 440–453, 2020.
[28] S. Li, J. Liu, and Z. Song, “Brain tumor segmentation based on region of interest-aided localization and segmentation U-Net,” International Journal of Machine Learning and Cybernetics, vol. 13, pp. 2435–2445, 2022.
[29] A. Kumar, “Study and analysis of different segmentation methods for brain tumor MRI application,” Multimedia Tools and Applications, pp. 7117–7139, 2023.
[30] S. Saeedi, S. Rezayi, H. Keshavarz, and S. R. Niakan Kalhori, “MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques,” BMC Medical Informatics and Decision Making, vol. 23, pp. 1–17, 2023.
[31] D. Lamrani, B. Cherradi, O. El Gannour, M. A. Bouqentar, and L. Bahatti, “Brain tumor detection using MRI images and convolutional neural network,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 7, pp. 452–460, 2022.
[32] A. Younis, L. Qiang, C. O. Nyatega, M. J. Adamu, and H. B. Kawuwa, “Brain tumor analysis using deep learning and VGG-16 ensembling learning approaches,” Applied Sciences, vol. 12, no. 14, pp. 1–20, 2022.
[33] F. J. D´ıaz-Pernas, M. Mart´ınez-Zarzuela, M. Ant´on-Rodr´ıguez, and D. Gonz´alez-Ortega, “A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network,” Healthcare, vol. 9, no. 2, pp. 1–14, 2021.
[34] M. Masood et al., “A novel deep learning method for recognition and classification of brain tumors from MRI images,” Diagnostics, vol. 11, no. 5, pp. 1–18, 2021.
[35] A. C¸ inar and M. Yildirim, “Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture,” Medical Hypotheses, vol. 139, 2020.
[36] N. Noreen, S. Palaniappan, A. Qayyum, I. Ahmad, M. Imran, and M. Shoaib, “A deep learning model based on concatenation approach for the diagnosis of brain tumor,” IEEE Access, vol. 8, pp. 55 135–55 144, 2020.
[37] S. A. A. Ismael, A. Mohammed, and H. Hefny, “An enhanced deep learning approach for brain cancer MRI images classification using residual networks,” Artificial Intelligence in Medicine, vol. 102, 2020.
[38] Kosta, “Brain tumor classification with simple CNN,” 2021. [Online]. Available: https://www.kaggle.com/code/boneacrabonjac/brain-tumor-classification-with-simple-cnn
[39] J. Bleker et al., “A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics,” European radiology, vol. 32, pp. 6526–6535, 2022.
[40] A. M. Saleh and T. Hamoud, “Analysis and best parameters selection for person recognition based on gait model using CNN algorithm and image augmentation,” Journal of Big Data, vol. 8, pp. 1–20, 2021.
[41] X. Shen et al., “Spatially-consistent feature matching and learning for heritage image analysis,” International Journal of Computer Vision, vol. 130, pp. 1325–1339, 2022.
[42] P. Singh, “A type-2 neutrosophic-entropy-fusion based multiple thresholding method for the brain tumor tissue structures segmentation,” Applied Soft Computing, vol. 103, 2021.
[43] V. Gonz´alez-Ruiz, M. R. Fern´andez-Fern´andez, and J. J. Fern´andez, “Structure-preserving gaussian denoising of FIB-SEM volumes,” Ultramicroscopy, vol. 246, pp. 1–11, 2023.
[44] D. Aarthi, A. Panimalar, S. S. Kumar, and K. Anitha, “Development of adaptive Gaussian filter based denoising as an image enhancement technique,” Mathematical Modelling of Engineering Problems, vol. 11, no. 10, pp. 2715–2725, 2024.
[45] S. S. Kolmar and C. M. Grulke, “The effect of noise on the predictive limit of QSAR models,” Journal of Cheminformatics, vol. 13, pp. 1–19, 2021.
[46] S. Lim, M. Park, H. Kim, S. H. Kang, K. Kim, and Y. Lee, “Optimization of median modified Wiener filter for improving lung segmentation performance in low-dose computed tomography images,” Applied Sciences, vol. 13, no. 19, pp. 1–13, 2023.
[47] R. Su, D. Zhang, J. Liu, and C. Cheng, “MSUNet: Multi-scale U-Net for 2D medical image segmentation,” Frontiers in Genetics, vol. 12, pp. 1–14, 2021.
[48] B. Brede et al., “Assessment of workflow feature selection on forest LAI prediction with sentinel-2A MSI, landsat 7 ETM+ and Landsat 8 OLI,” Remote Sensing, vol. 12, no. 6, pp. 1–29, 2020.
[49] I. M. Mohammed and N. A. M. Isa, “Contrast limited adaptive local histogram equalization method for poor contrast image enhancement,” IEEE Access, vol. 13, pp. 62 600–62 632, 2025.
[50] Y. J. Ng and K. S. Sim, “A review of brain early infarct image contrast enhancement using various histogram equalization techniques,” International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 6, pp. 1849–1860, 2024.
[51] K. Halloum and H. Ez-Zahraouy, “Enhancing medical image classification through transfer learning and CLAHE optimization,” Current Medical Imaging, vol. 21, no. 1, pp. 1–14, 2025.
[52] A. Zhang and A. S. Zhang, “Real-time wildfire detection and alerting with a novel machine learning approach,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 8, pp. 1–6, 2022.
[53] A. Cromb´e et al., “Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients,” Scientific Reports, vol. 10, pp. 1–13, 2020.
[54] Z. Li et al., “A systematic survey of regularization and normalization in GANs,” ACM Computing Surveys, vol. 55, no. 11, pp. 1–37, 2023.
[55] P. Gupta, P. Jain, and O. G. Kakde, “Accelerating deep network training for radar identification using batch normalization,” Defence Science Journal, vol. 74, no. 6, pp. 878–884, 2024.
[56] A. Kairys and V. Raudonis, “Analysis of training data augmentation for diabetic foot ulcer semantic segmentation,” Electronics, vol. 12, no. 22, pp. 1–14, 2023.
[57] E. K. Kim, H. Lee, J. Y. Kim, and S. Kim, “Data augmentation method by applying color perturbation of inverse PSNR and geometric transformations for object recognition based on deep learning,” Applied Sciences, vol. 10, no. 11, pp. 1–20, 2020.
[58] V. R. Joseph and A. Vakayil, “SPlit: An optimal method for data splitting,” Technometrics, vol. 64, no. 2, pp. 166–176, 2022.
[59] M. Rosenblatt, L. Tejavibulya, R. Jiang, S. Noble, and D. Scheinost, “Data leakage inflates prediction performance in connectome-based machine learning models,” Nature Communications, vol. 15, pp. 1–15, 2024.
[60] V. Singh, M. Pencina, A. J. Einstein, J. X. Liang, D. S. Berman, and P. Slomka, “Impact of train/test sample regimen on performance estimate stability of machine learning in cardiovascular imaging,” Scientific reports, vol. 11, pp. 1–8, 2021.
[61] Y. Liu, D. Cheng, D. Zhang, S. Xu, and J. Han, “Capsule networks with residual pose routing,” IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 2, pp. 2648–2661, 2024.
[62] R. Haque, M. M. Hassan, A. K. Bairagi, and S. M. Shariful Islam, “NeuroNet19: An explainable deep neural network model for the classification of brain tumors using magnetic resonance imaging data,” Scientific Reports, vol. 14, pp. 1–22, 2024.
[63] P. K. Pandey, J. Pruthi, S. Alzahrani, A. Verma, and B. Zohra, “Enhancing healthcare recommendation: transfer learning in deep convolutional neural networks for Alzheimer disease detection,” Frontiers in Medicine, vol. 11, pp. 1–12, 2024.
[64] K. V. Priya and J. D. Peter, “A federated approach for detecting the chest diseases using DenseNet for multi-label classification,” Complex & Intelligent Systems, vol. 8, no. 4, pp. 3121–3129, 2022.
[65] R. Wang, T. Lei, R. Cui, B. Zhang, H. Meng, and A. K. Nandi, “Medical image segmentation using deep learning: A survey,” IET image processing, vol. 16, no. 5, pp. 1243–1267, 2022.
[66] A. M. Alhassan and N. I. Altmami, “IV3TM: Inception V3 enabled bidirectional long short-term memory network for brain tumor classification,” PLOS One, vol. 20, no. 10, pp. 1–34, 2025.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Tegar Anugrah Firdaus, Bryan Rais, Marcelinus Jonathan Salim, Andhika Rastra Negara, Muhammad Qaiser Zaydan Zia, Yamin Thwe, Muhammad Nur Afnan Uda, Uda Hashim, Yuri Pamungkas
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License - Share Alike that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Â
USER RIGHTS
All articles published Open Access will be immediately and permanently free for everyone to read and download. We are continuously working with our author communities to select the best choice of license options, currently being defined for this journal as follows: Creative Commons Attribution-Share Alike (CC BY-SA)
