A novel algorithmic approach of progressive transfer learning for MRI braintumor classification using VGG16 architecture
*Sunil Kumar AgarwalCorresponding authorsun.aipc@gmail.comDepartment of Computer ScienceBanasthali VidyapithJaipur, Rajasthan, 304022, IndiaView full profile → , Yogesh Kumar Guptagyogesh@banasthali.inDepartment of Computer ScienceBanasthali VidyapithJaipur, Rajasthan, 304022, IndiaView full profile → , Pankaj Dadheechpankajdadheech777@gmail.comDepartment of Computer Science & EngineeringSwami Keshwanand Institute of Technology, Management & Gramothan (SKIT)Jaipur, Rajasthan, 302017, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Sep 2025
- Published Online:
- 09 Apr 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIM-2572
- Pages:
- 1023–1031
Abstract
Keywords
Subject Classifications
References
[1] 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. 34716-34730 (2022).
[2] K. Kaul, A. Chug, A. P. Singh, R. Kumar, and V. Shanmugam, “Hyperparameter tuning of CNN based potato plant disease detection model integrated with GridSearchCV,” J. Inf. Optim. Sci., vol. 46, no. 1, pp. 1-14 (2025), doi: 10.47974/JIOS-1846.
[3] N. A. Baker, N. Zengeler, and U. Handmann, “A transfer learning evaluation of deep neural networks for image classification,” Mach. Learn. Knowl. Extr., vol. 4, no. 1, pp. 22-41 (2022).
[4] M. S. Basha, K. R. Prasad, S. K. Mouleeswaran, R. C. Poonia, and S. Sebastian, “Multi-disease detection system with X-ray images using deep learning techniques,” J. Inf. Optim. Sci., vol. 45, no. 5, pp. 1379–1388 (2024), doi: 10.47974/JIOS-1710.
[5] S. F. Ahmed, M. S. B. Alam, M. Hassan, M. R. Rozbu, T. Ishtiak, N. Rafa, and A. H. Gandomi, “Deep learning modelling techniques: current progress, applications, advantages, and challenges,” Artif. Intell. Rev., vol. 56, no. 11, pp. 13521-13617 (2023).
[6] S. M. Ghoreishi Mokri, N. Valadbeygi, and V. Grigoryeva, “Diagnosis of glioma, meningioma and pituitary brain tumor using MRI images recognition by deep learning in Python,” EAI Endorsed Trans. Intell. Syst. Mach. Learn. Appl., vol. 1, no. 1, p. 9 (Apr. 2024), doi: 10.4108/eetismla.5410
[7] M. Iman, H. R. Arabnia, and K. Rasheed, “A review of deep transfer learning and recent advancements,” Technologies, vol. 11, no. 2, Art. no. 40 (2023).
[8] L. Beyer, O. J. Hénaff, A. Kolesnikov, X. Zhai, and A. V. D. Oord, “Are we done with ImageNet?,” arXiv:2006.07159 (2020).
[9] T. Kattenborn, J. Leitloff, F. Schiefer, and S. Hinz, “Review on Convolutional Neural Networks (CNN) in vegetation remote sensing,” ISPRS J. Photogramm. Remote Sens., vol. 173, pp. 24-49 (2021).
[10] M. V. Gupta, R. K. Dwivedi, and A. Kumar, “Impact analysis of convolutional neural network in classification of satellite imagery,” J. Inf. Optim. Sci., vol. 44, no. 6, pp. 1151–1166 (2023), doi: 10.47974/JIOS-1453.
[11] C. Öztürk, M. Taşyürek, and M. U. Türkdamar, “Transfer learning and fine-tuned transfer learning methods’ effectiveness analyse in the CNN-based deep learning models,” Concurrency Computat. Pract. Exper., vol. 35, no. 4, Art. no. e7542 (2023).
[12] H. S. AlSagri, S. S. Sohail, and S. Sebastian, “The role of deep reinforcement learning in developing adaptive cybersecurity defenses for smart grid systems,” J. Inf. Optim. Sci., vol. 45, no. 8, pp. 2299–2307 (2024), doi: 10.47974/JIOS-1807.
[13] A. M. Carrington, D. G. Manuel, P. W. Fieguth, T. Ramsay, V. Osmani, B. Wernly, and A. Holzinger, “Deep ROC analysis and AUC as balanced average accuracy, for improved classifier selection, audit and explanation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 1, pp. 329-341 (2022).
[14] T. Lee, V. P. Singh, and K. H. Cho, “Tensorflow and keras programming for deep learning,” in Deep Learning for Hydrometeorology and Environmental Science, Cham, Switzerland: Springer International Publishing, pp. 151–162 (2021).
