A Novel Approach of Progressive Transfer Learning for MRI Brain Tumor Classification Using VGG16 and MobileNet Architectures

Sunil Kumar Agarwal; Yogesh Kumar Gupta

doi:10.62762/TMI.2025.367009

Volume 2, Issue 1, ICCK Transactions on Machine Intelligence

Volume 2, Issue 1, 2026

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ICCK Transactions on Machine Intelligence, Volume 2, Issue 1, 2026: 28-37

Free to Read | Research Article | 07 January 2026

A Novel Approach of Progressive Transfer Learning for MRI Brain Tumor Classification Using VGG16 and MobileNet Architectures

Sunil Kumar Agarwal 1 *

Yogesh Kumar Gupta 1

1 Department of Computer Science, Banasthali Vidyapith, Rajasthan 304022, India

* Corresponding Author: Sunil Kumar Agarwal, [email protected]

DOI: 10.62762/TMI.2025.367009

ARK: ark:/57805/tmi.2025.367009

Received: 11 November 2025, Accepted: 26 November 2025, Published: 07 January 2026

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Abstract

Around the world, brain tumors are a major cause of human mortality. Accurate brain tumor detection is essential for effective treatment and improved patient outcomes. This study introduces the progressive transfer learning method, using VGG16 and MobileNet for the brain tumor identification and classification task. The outcome demonstrated the importance of the proposed models. The final accuracy of VGG16 and MobileNet on the test data was 98% and 87%, respectively, highlighting the superiority of VGG16 over the MobileNet framework. In addition, future work suggests advanced fine-tuning strategies, regularization techniques, and other methods to improve model performance for helping medical professionals in brain tumor diagnosis.

Graphical Abstract

Keywords

brain tumor classification

transfer learning

VGG16

MobileNet

model fine-tuning

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

Not applicable.

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APA Style

Agarwal, S. K., & Gupta, Y. K. (2026). A Novel Approach of Progressive Transfer Learning for MRI Brain Tumor Classification Using VGG16 and MobileNet Architectures. ICCK Transactions on Machine Intelligence, 2(1), 28–37. https://doi.org/10.62762/TMI.2025.367009

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TY  - JOUR
AU  - Agarwal, Sunil Kumar
AU  - Gupta, Yogesh Kumar
PY  - 2026
DA  - 2026/01/07
TI  - A Novel Approach of Progressive Transfer Learning for MRI Brain Tumor Classification Using VGG16 and MobileNet Architectures
JO  - ICCK Transactions on Machine Intelligence
T2  - ICCK Transactions on Machine Intelligence
JF  - ICCK Transactions on Machine Intelligence
VL  - 2
IS  - 1
SP  - 28
EP  - 37
DO  - 10.62762/TMI.2025.367009
UR  - https://www.icck.org/article/abs/TMI.2025.367009
KW  - brain tumor classification
KW  - transfer learning
KW  - VGG16
KW  - MobileNet
KW  - model fine-tuning
AB  - Around the world, brain tumors are a major cause of human mortality. Accurate brain tumor detection is essential for effective treatment and improved patient outcomes. This study introduces the progressive transfer learning method, using VGG16 and MobileNet for the brain tumor identification and classification task. The outcome demonstrated the importance of the proposed models. The final accuracy of VGG16 and MobileNet on the test data was 98% and 87%, respectively, highlighting the superiority of VGG16 over the MobileNet framework. In addition, future work suggests advanced fine-tuning strategies, regularization techniques, and other methods to improve model performance for helping medical professionals in brain tumor diagnosis.
SN  - 3068-7403
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Agarwal2026A,
  author = {Sunil Kumar Agarwal and Yogesh Kumar Gupta},
  title = {A Novel Approach of Progressive Transfer Learning for MRI Brain Tumor Classification Using VGG16 and MobileNet Architectures},
  journal = {ICCK Transactions on Machine Intelligence},
  year = {2026},
  volume = {2},
  number = {1},
  pages = {28-37},
  doi = {10.62762/TMI.2025.367009},
  url = {https://www.icck.org/article/abs/TMI.2025.367009},
  abstract = {Around the world, brain tumors are a major cause of human mortality. Accurate brain tumor detection is essential for effective treatment and improved patient outcomes. This study introduces the progressive transfer learning method, using VGG16 and MobileNet for the brain tumor identification and classification task. The outcome demonstrated the importance of the proposed models. The final accuracy of VGG16 and MobileNet on the test data was 98\% and 87\%, respectively, highlighting the superiority of VGG16 over the MobileNet framework. In addition, future work suggests advanced fine-tuning strategies, regularization techniques, and other methods to improve model performance for helping medical professionals in brain tumor diagnosis.},
  keywords = {brain tumor classification, transfer learning, VGG16, MobileNet, model fine-tuning},
  issn = {3068-7403},
  publisher = {Institute of Central Computation and Knowledge}
}

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