Enhanced transfer learning framework for brain tumor detection from MRI scans using attention-based feature fusion

Smita Dinesh Bharne, Ekta Sarda, Shamal Salunkhe

Abstract


Due to the complexity of the different tumor types in medical imaging detection of brain tumor is still as prominent challenge. This paper present the innovative technique Enhanced Transfer Learning Framework (ETLF) which integrating the advanced pre-processing with hybrid fine-tuned method for accurate brain tumor detection from MRI (Magnetic resonance imaging) scans. The proposed model combine the strength of pre-trained convolutional neural network (CNNs) such as EfficientNetB0 through domain specific transfer learning and attention based fine tuning. A novel feature fusion layer and adaptive learning rate scheduler are key indicators for model performance and prevent overfitting. The methodology is assessed on the benchmark dataset BraTS and Kaggle brain tumor datasets. The main contribution of work lies in development of domain- adaptive transfer learning with different datasets. The ETLF shows the high accuracy of 98.76% which able outperforms effectively in diagnosing tumor suitable of clinical purpose.

Keywords


Transfer learning; EfficientNet ;Fine-tuning; Deep learning;Brain tumor detection

Full Text:

PDF

References


M. El-Dahshan, H. Hosny, and A. Salem, “Hybrid intelligent techniques for MRI brain images classification,” Digital Signal Processing, vol. 20, no. 2, pp. 433–441, Mar. 2010.https://doi.org/10.1016/j.dsp.2009.07.002

Babu Vimala B, Srinivasan S, Mathivanan SK, Mahalakshmi, Jayagopal P, Dalu GT. Detection and classification of brain tumor using hybrid deep learning models. Scientific reports. 2023 Dec 27;13(1):23029.

Kumar KK, Dinesh PM, Rayavel P, Vijayaraja L, Dhanasekar R, Kesavan R, Raju K, Khan AA, Wechtaisong C, Haq MA, Alzamil ZS. Brain Tumor Identification Using Data Augmentation and Transfer Learning Approach. Computer Systems Science & Engineering. 2023 Aug 1;46(2).

Kamnitsas K, Ledig C, Newcombe VF, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical image analysis. 2017 Feb 1;36:61-78.

Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. InComputer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13 2014 (pp. 818-833). Springer International Publishing.

Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. InInternational conference on machine learning 2019 May 24 (pp. 6105-6114). PMLR.

Gupta BB, Gaurav A, Arya V. Deep CNN based brain tumor detection in intelligent systems. International Journal of Intelligent Networks. 2024 Jan 1;5:30-7.

Khaliki MZ, Başarslan MS. Brain tumor detection from images and comparison with transfer learning methods and 3-layer CNN. Scientific Reports. 2024 Feb 1;14(1):2664.

Mathivanan SK, Sonaimuthu S, Murugesan S, Rajadurai H, Shivahare BD, Shah MA. Employing deep learning and transfer learning for accurate brain tumor detection. Scientific Reports. 2024 Mar 27;14(1):723

Preetha R, Priyadarsini MJ, Nisha JS. Automated Brain Tumor Detection from Magnetic Resonance Images Using Fine-Tuned EfficientNet-B4 Convolutional Neural Network. IEEE Access. 2024 Aug 13

Rastogi D, Johri P, Donelli M, Kumar L, Bindewari S, Raghav A, Khatri SK. Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks. Life. 2025 Feb 20;15(3):327.

Disci R, Gurcan F, Soylu A. Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models. Cancers. 2025 Jan 2;17(1):121.

Pande Y, Chaki J. Brain tumor detection across diverse MR images: An automated triple-module approach integrating reduced fused deep features and machine learning. Results in Engineering. 2025 Mar 1;25:103832.

Islam MN, Azam MS, Islam MS, Kanchan MH, Parvez AS, Islam MM. An improved deep learning-based hybrid model with ensemble techniques for brain tumor detection from MRI image. Informatics in Medicine Unlocked. 2024 Jan 1;47:101483

Ali S, Khurram R, Rehman KU, Yasin A, Shaukat Z, Sakhawat Z, Mujtaba G. An improved 3D U-Net-based deep learning system for brain tumor segmentation using multi-modal MRI. Multimedia Tools and Applications. 2024 May 25:1-20.

Anantharajan S, Gunasekaran S, Subramanian T. MRI brain tumor detection using deep learning and machine learning approaches. Measurement: Sensors. 2024 Feb 1;31:101026.

Rezk NG, Alshathri S, Sayed A, Hemdan EE, El-Behery H. Secure Hybrid Deep Learning for MRI-Based Brain Tumor Detection in Smart Medical IoT Systems. Diagnostics. 2025 Mar 6;15(5):639.

Gupta BB, Gaurav A, Arya V. Deep CNN based brain tumor detection in intelligent systems. International Journal of Intelligent Networks. 2024 Jan 1;5:30-7.

Ahamed MF, Hossain MM, Nahiduzzaman M, Islam MR, Islam MR, Ahsan M, Haider J. A review on brain tumor segmentation based on deep learning methods with federated learning techniques.

Computerized Medical Imaging and Graphics. 2023 Dec 1;110:102313.

Mathivanan SK, Sonaimuthu S, Murugesan S, Rajadurai H, Shivahare BD, Shah MA. Employing deep learning and transfer learning for accurate brain tumor detection. Scientific Reports. 2024 Mar 27;14(1):7232.Disci R, Gurcan F, Soylu A. Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models. Cancers. 2025 Jan 2;17(1):121

Disci R, Gurcan F, Soylu A. Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models. Cancers. 2025 Jan 2;17(1):121.

https://www.med.upenn.edu/cbica/brats2020/data.html

B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) DOI: 10.1109/TMI.2014.2377694

S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J.S. Kirby, et al., "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data, 4:170117 (2017) DOI: 10.1038/sdata.2017.117

S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, et al., "Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge", arXiv preprint arXiv:1811.02629 (2018)

https://www.kaggle.com/datasets/pkdarabi/medical-image-dataset-brain-tumor-detection, accessed online 30th January 2025.




DOI: http://doi.org/10.11591/ijict.v15i2.pp497-507

Refbacks

  • There are currently no refbacks.


Copyright (c) 2026 Institute of Advanced Engineering and Science

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

The International Journal of Informatics and Communication Technology (IJ-ICT)
p-ISSN 2252-8776, e-ISSN 2722-2616
This journal is published by the Intelektual Pustaka Media Utama (IPMU).

Web Analytics View IJICT Stats