MRI-Based Brain Tumor Detection Using Transfer Learning: Comparative Analysis of CNN Models and a Hybrid Ensemble Approach

Kusaji P. Gawas, Prathamesh G. Harmalkar and Tejas V. Joshi

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 3, 1041-1052

Abstract: Early and precise identification of brain tumors is essential for improving diagnostic accuracy and supporting timely medical intervention. The analysis of MRIs manually is quite time-consuming and demands expertise. To mitigate the previously identified challenges, an intelligent machine learning approach is developed for accurate brain tumor detection and classification from MRI scans. For this purpose, a set of convolutional neural networks such as ResNet50, DenseNet121, Xception, EfficientNetV2B0 has been utilized. These deep learning models employ transfer learning to extract meaningful features from medical images. Each model was trained separately and evaluated depending on accuracy, precision, recall, f1-score, ROC-AUC. The performance of all four models is compared to determine their strengths and weaknesses. Furthermore, we tried merging the results obtained from each model. Based on the obtained results, the ensemble approach demonstrates superior performance compared to standalone models.

Keywords: Brain Tumor Detection; MRI; Deep Learning; Transfer Learning; Hybrid Model; Ensemble Learning; Convolutional Neural Networks (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrst.com/home/article/view/IJSRST26133232 Abstract page (text/html)
https://ijsrst.com/home/article/download/IJSRST26133232/IJSRST26133232 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i3:id:1696

DOI: 10.32628/IJSRST26133232

Access Statistics for this article

More articles in International Journal of Scientific Research in Science and Technology from Technoscience Academy
Bibliographic data for series maintained by Pankaj Sharma ().