A Novel Approach of Progressive Transfer Learning for MRI Brain Tumor Classification Using VGG16 and MobileNet Architectures
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.
Fields of Study & Subjects
Brain Tumor Detection and Classification
Advanced Neural Network Applications
Scientific and Engineering Research Topics
Publication details
- Journal / source
- ICCK Transactions on Machine Intelligence
- Publisher
- Institute of Central Computation and Knowledge Inc.
- Volume
- 2
- Issue
- 1
- Pages
- 28–37
- Publication date
- 7 January 2026