Deep Learning Based Brain Tumor Diagnosis

Abstract

Among all oncology disorders, brain tumors are among the most complex and lethal, necessitating rapid and accurate differential diagnosis. Traditional manual examination of Magnetic Resonance Imaging (MRI) scans is time-consuming and subject to significant variability among radiologists. This study evaluates five deep learning architectures — InceptionV3, ResNet50, VGG16, InceptionResNetV2, and EfficientNetV2L — for the automated classification of brain tumors into glioma, meningioma, and pituitary tumor types using the Figshare Brain Tumor Classification dataset. The dataset was split into 60% training, 20% validation and 20% testing sets with class weights addressing class imbalance. Models were initialized with ImageNet pre-trained weights, with custom layers (GlobalAveragePooling2D, dense layers with 1024–4096 units, and dropout) added for feature extraction and classification. They were trained for 30 epochs using the Adam optimizer. The experimental results show that the InceptionV3 model achieved an accuracy of 92.10% and also demonstrated better Grad-CAM performance for the effective localization of tumor regions than the other four algorithms. These results highlight the potential of deep learning, particularly InceptionV3, to enhance diagnostic accuracy and efficiency in brain tumor classification. Future research should focus on refining model architectures and optimizing computations for real-time clinical applications.

Keywords

• Magnetic Resonance Imaging (MRI)
• Brain tumor
• Deep learning
• GradCAM

References

1. A. A. Rowden, “Types, symptoms, and treatment of a brain tumor.” Medical News Today [Online]. Available: https://www.medicalnewstoday.com/articles/315625
2. M. Pichaivel, G. Anbumani, P. Theivendren, and M. Gopal, “An overview of brain tumor,” IntechOpen, 2022, doi: 10.5772/intechopen.100806
3. K. Malleswari, D. R. Reddy, and K Smily, “Brain cancer,” International Journal for Multidisciplinary Research, vol. 6, no. 1, Jan.- Feb. 2024, doi: 10.36948/ijfmr.2024.v06i01.13029.
4. M. D. Vinoparkavi, P. Pradeep, M. D. Aparna, A. G. Kavin, and M. P. Durai, “Efficient classification of brain tumor images using neural network technique,” Int. Sci. J. Eng. Manag., vol. 2, no. 04, pp. 1–8, 2023, doi: 10.55041/ISJEM00204.
5. A. Çinar and M. Yildirim, “Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture,” Med. Hypotheses, vol. 139, Jun. 2020, doi: 10.1016/j.mehy.2020.109684.
6. D. Crosby et al., “Early detection of cancer,” American Association for the Advancement of Science, 2022, doi: 10.1126/science. aay9040.
7. M. Sajjad, S. Khan, K. Muhammad, W. Wu, A. Ullah, and S. W. Baik, “Multi-grade brain tumor classification using deep CNN with extensive data augmentation,” J. Comput. Sci., vol. 30, pp. 174–182, Jan. 2019, doi: 10.1016/j.jocs.2018.12.003.
8. A. B. Abdusalomov, M. Mukhiddinov, and T. K. Whangbo, “Brain tumor detection based on deep learning approaches and magnetic resonance imaging,” Cancers (Basel), vol. 15, no. 16, Aug. 2023, doi: 10.3390/cancers15164172.

9. T. Hussain and H. Shouno, “Explainable deep learning approach for multi-class brain magnetic resonance imaging tumor classification and localization using gradient-weighted class activation mapping,” Information, vol. 14, no. 12, Art. no. 642, Nov. 2023, doi: 10.3390/info14120642.
10. Q. Teng, Z. Liu, Y. Song, K. Han, and Y. Lu, “A survey on the interpretability of deep learning in medical diagnosis.” Multimed. Syst., vol. 28, no. 6, pp. 2335–2355, Jun. 2022, doi: 10.1007/s00530-022-00960-4.
11. C. Rajeshkumar, K. R. Soundar, M. Sneha, S. S. Maheswari, M. S. Lakshmi, and R. Priyanka, “Convolutional neural networks (CNN) based brain tumor detection in MRI images,” in Proc. 5th Int. Conf. Smart Syst. Inventive Technol. (ICSSIT), 2023, pp. 976–979.
12. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp. 770–778.
13. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv:1409.1556, Sep. 2014. [Online]. Available: http://arxiv.org/abs/1409.1556
14. G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” Aug. 2016, [Online]. Available: http://arxiv.org/abs/1608.06993
15. M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” arXiv:1905.11946, May. 2019, [Online]. Available: http://arxiv.org/abs/1905.11946
16. N. A. Mohamed and M. J. Deen, “Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images,” Comput. Biol. Med., vol. 121, p. 103758, Apr. 2020, doi: 10.1016/j.compbiomed.2020.103758.
17. M. M. Zahoor et al., “Brain tumor MRI classification using a novel deep residual network,” Biomedicines, pp. 1–19, 2024, doi: 10.3390/biomedicines12071395.
18. H. Dave, N. Kant, N. Dave, and D. Ghorui, “Brain tumor classification using deep learning,” 2021. [Online]. Available: http://www.ijeast.com
19. M. M. Badža and M. C. Barjaktarović, “Classification of brain tumors from MRI images using a convolutional neural network,” Appl. Sci. vol. 10, no. 6, Mar. 2020, doi: 10.3390/app10061999.
20. M. I. Sharif, M. A. Khan, M. Alhussein, K. Aurangzeb, and M. Raza, “A decision support system for multimodal brain tumor classification using deep learning,” Complex Intell. Syst., vol. 8, no. 4, pp. 3007–3020, Aug. 2022, doi: 10.1007/s40747-021-00321-0.
21. M. Naufal, H. Al Azies, and R. M. Brilianto, “Enhanced brain tumor classification through gamma correction in deep learning,” Sistemasi, vol. 13, no. 6, p. 2348, 2024, doi: 10.32520/stmsi.v13i6.4474.
22. J. Sekar, S. Karthikeyan, and S. Saravanakumar, “An efficient brain tumor classification and segmentation using deep learning features for ML algorithms,” J. Neonatal Surg., vol. 14, no. 11, pp. 417–425, 2025.
23. M. R. Ismael and I. Abdel-Qader, “Brain tumor classification via statistical features and back-propagation neural network,” in Proc. IEEE Int. Conf. Electro/Inf. Technol. (EIT), 2018, pp. 252–257.
24. W. Ayadi, I. Charfi, W. Elhamzi, and M. Atri, “Brain tumor classification based on a hybrid approach,” Vis. Comput., vol. 38, no. 1, pp. 107-117, 2022.
25. N. Abiwinanda, M. Hanif, S. T. Hesaputra, A. Handayani, and T. R. Mengko, “Brain tumor classification using convolutional neural network,” in Proc. World Congr. Med. Phys. Biomed. Eng., Prague, Czech Republic, Jun. 3–8, 2018, pp. 183–189, 2019.
26. Kaggle, “Brain Tumor Image Dataset.” [Online]. Available: https://www.kaggle.com/datasets/denizkavi1/brain-tumor
27. E. Ranjith, L. Parthiban, T. Latchoumi, K. Anand, P. Darshika, and R. Sangeetha, “An effective content-based image retrieval system using deep learning-based inception model,” Wirel. Pers. Commun., vol. 133, Art. no. 19, Jan. 2024, doi: 10.1007/s11277-023-10792-8
28. B. Lokula, R. Tirumur, and V. N. Prasad, “Satellite image segmentation using U-Net++ and MobileNetV2 deep learning model,” Res. Sq., pp. 0–24, Marc. 2024, doi: 10.21203/rs.3.rs-4144393/v1.
29. D. Cho, W. Baek, D. Kim, and J. Kim, “UAV-driven structural crack detection and location determination using convolutional neural networks,” Sensors, vol. 21, no. 8, Art. No. 2650, 2021, doi: 10.3390/s21082650
30. N. Martinus, S. Jason, and J. Kelvin, “AniWho: A quick and accurate way to classify anime character faces in images,” Inf. Syst. J., Aug. 2022, doi: 10.48550/arXiv.2208.11012
31. U. Waqas, J. W. Visser, H. Choe and D. Lee, “Multimodal Fused Deep Learning Networks for Domain Specific Image Similarity Search,” Computers, Materials and Continua, vol. 75, no. 1, pp. 243-258, 2023, doi: 10.32604/cmc.2023.035716.