Advancements in Brain Tumor Detection using Transfer Learning Model with CNN-based Multi-Head Attention for Tumor Type Classification

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 7

Year of Publication : 2025

Authors : V. Chanemougavel, K. Jayanthi

10.14445/23488549/IJECE-V12I7P102

How to Cite?

V. Chanemougavel, K. Jayanthi, "Advancements in Brain Tumor Detection using Transfer Learning Model with CNN-based Multi-Head Attention for Tumor Type Classification," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 7, pp. 13-23, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I7P102

Abstract:

Brain Tumour (BT) is a common and aggressive disorder that leads to a very short life expectancy in high-grade grades. Therefore, treatment planning is the primary phase in enhancing the quality of patient care. Numerous image models like Computed Tomography (CT), ultrasound imaging, and Magnetic Resonance Imaging (MRI) were employed to assess a brain's cancer region. Compared to other image techniques, MRI images are used to analyze the tumour in the brain. Conversely, the enormous amount of data generated by MRI scans prevents the manual classification of non-tumours and tumours in a precise manner. However, it is time-consuming and requires knowledge to examine the MRI imaging. Currently, the development of Computer-Aided Diagnosis (CAD), Machine Learning (ML), and Deep Learning (DL) models permits the expert to recognize BT. This manuscript presents a Hybrid Artificial Intelligence-Based Brain Tumor Classification with Transfer Learning and Metaheuristic Optimization (HAIBTC-TLMO) model using MRI imaging. The HAIBTC-TLMO model aims to develop an effective and accurate method for classifying BTs. Image pre-processing begins with noise removal using a bilateral filter (BF), followed by skull removal through Otsu thresholding and morphological operations. Moreover, the proposed HAIBTC-TLMO model utilizes the NASNetMobile method for feature extraction to detect the BT region from the input image data. The hybrid Graph Convolutional Gated Recurrent Network (GCGRN) method is also employed for classification. Finally, the Spotted Hyena Optimizer (SHO) optimally adjusts the hyperparameters of the GCGRN method, resulting in improved classification performance. The experimental analysis of the HAIBTC-TLMO approach is conducted on a BT MRI dataset. The performance validation of the HAIBTC-TLMO approach demonstrated a superior accuracy output of 94.64% over existing methods.

Keywords:

Artificial Intelligence, Brain tumor classification, Transfer learning, Metaheuristic optimization, MRI.

References:

[1] Nyoman Abiwinanda et al., “Brain Tumor Classification Using Convolutional Neural Network,” World Congress on Medical Physics and Biomedical Engineering 2018, Prague, Czech Republic, vol. 1, pp. 183-189, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Wadhah Ayadi et al., “Deep CNN for Brain Tumor Classification,” Neural Processing Letters, vol. 53, pp. 671-700, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Ahmad Saleh, Rozana Sukaik, and Samy S. Abu-Naser, “Brain Tumor Classification Using Deep Learning,” 2020 International Conference on Assistive and Rehabilitation Technologies, Gaza, Palestine, pp. 131-136, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Asaf Raza et al., “A Hybrid Deep Learning-Based Approach for Brain Tumor Classification,” Electronics, vol. 11, no. 7, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Hassan Ali Khan et al., “Brain Tumor Classification in MRI Image Using Convolutional Neural Network,” Mathematical Biosciences and Engineering, vol. 17, no. 5, pp. 6203-6216, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Dillip Ranjan Nayak et al., “Brain Tumor Classification Using Dense Efficient-Net,” Axioms, vol. 11, no. 1, pp. 1-13, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Muhammad Aamir et al., “A Deep Learning Approach for Brain Tumor Classification Using MRI Images,” Computers and Electrical Engineering, vol. 101, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Isselmou Abd El Kader et al., “Differential Deep Convolutional Neural Network Model for Brain Tumor Classification,” Brain Sciences, vol. 11, no. 3, pp. 1-16, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Osman Özkaraca et al., “Multiple Brain Tumor Classification with Dense CNN Architecture Using Brain MRI Images,” Life, vol. 13, no. 2, pp. 1-16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Ehsan khodadadi, S.K. Towfek, and Hussein Alkattan, “Brain Tumor Classification Using Convolutional Neural Network and Feature Extraction,” Fusion: Practice & Applications, vol. 13, no. 2, pp. 34-41, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Sofia El Amoury, Youssef Smili, and Youssef Fakhri, “Design of an Optimal Convolutional Neural Network Architecture for MRI Brain Tumor Classification by Exploiting Particle Swarm Optimization,” Journal of Imaging, vol. 11, no. 2, pp. 1-19, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Akmalbek Abdusalomov et al., “Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments,” Bioengineering, vol. 11, no. 6, pp. 1-23, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] A. Maria Nancy, and R. Maheswari, “Brain Tumor Segmentation and Classification Using Transfer Learning Based CNN Model with Model Agnostic Concept Interpretation,” Multimedia Tools and Applications, vol. 84, pp. 2509-2538, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[14] M. Akshay Kumaar et al., “Brain Tumor Classification Using a Pre-Trained Auxiliary Classifying Style-Based Generative Adversarial Network,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 8, no. 6, pp. 101-111, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Chandni, Monika Sachdeva, and Alok Kumar Singh Kushwaha, “AI-Based Intelligent Hybrid Framework (BO-DenseXGB) for Multi- Classification of Brain Tumor Using MRI,” Image and Vision Computing, vol. 154, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Akshay Bhuvaneswari Ramakrishnan et al., “Optimizing Brain Tumor Classification with Hybrid CNN Architecture: Balancing Accuracy and Efficiency through One API Optimization,” Informatics in Medicine Unlocked, vol. 44, pp. 1-8, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Hemant Kumar et al., “A Hybrid EfficientNetB0-XGBoost Framework for Efficient Brain Tumor Classification Using MRI Images,” Internet of Everything, and Machine Learning, pp. 519-534, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Anees Tariq et al., “Transforming Brain Tumor Detection Empowering Multi-Class Classification With Vision Transformers and EfficientNetV2,” IEEE Access, vol. 13, pp. 63857-63876, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Md Saiful Islam Sajol, and A.S.M Jahid Hasan, “Benchmarking CNN and Cutting-Edge Transformer Models for Brain Tumor Classification Through Transfer Learning,” 2024 IEEE 12th International Conference on Intelligent Systems, pp. 1-6, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Yu Wang et al., “MSegNet: A Multi-View Coupled Cross-Modal Attention Model for Enhanced MRI Brain Tumor Segmentation,” International Journal of Computational Intelligence Systems, vol. 18, pp. 1-32, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Yinyi Lai et al., “Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning,” Arxiv, pp. 1-10, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Zhiyong Li, and Xinlian Zhou, “A Global-Local Parallel Dual-Branch Deep Learning Model with Attention-Enhanced Feature Fusion for Brain Tumor MRI Classification,” Computers, Materials & Continua, vol. 83, no. 1, pp. 739-760, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Ishak Pacal et al., “Enhancing EfficientNetv2 with Global and Efficient Channel Attention Mechanisms for Accurate MRI-Based Brain Tumor Classification,” Cluster Computing, vol. 27, pp. 11187-11212, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[24] R. Preetha, M. Jasmine Pemeena Priyadarsini, and J.S. Nisha, “Hybrid 3B Net and EfficientNetB2 Model for Multiclass Brain Tumor Classification,” IEEE Access, vol. 13, pp. 63465-63485, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[25] B. Srinivas et al., “A Fine-Tuned Transformer Model for Brain Tumor Detection and Classification,” Multimedia Tools and Applications, vol. 84, pp. 15597-15621, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Assma G. Jaber, and Mohammed A. Mohammed, “Using Statistical Non-Linear Filters for Gaussian Carina Nebula Image Denoising,” Journal of Al-Rafidain University College For Sciences, vol. 56, no. 1, pp. 431-443, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[27] M. Anlin Sahaya Infant Tinu, Ahilan Appathurai, and N. Muthukumaran, “Detection of Brain Tumour Via Reversing Hexagonal Feature Pattern for Classifying Double-Modal Brain Images,” IETE Journal of Research, vol. 70, no. 8, pp. 7033-7043, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Marzieh Ghahramani, and Nabiollah Shiri, “An Adaptive Neuro-Fuzzy Inference System Optimized by Genetic Algorithm for Brain Tumour Detection in Magnetic Resonance Images,” IET Image Processing, vol. 18, no. 5, pp. 1358-1372, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Farid et al., “Bangladeshi Vehicle Classification and Detection Using Deep Convolutional Neural Networks with Transfer Learning,” IEEE Access, vol. 13, pp. 26429-26455, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Lei Wang et al., “A Gated Graph Convolutional Network with Multi-Sensor Signals for Remaining Useful Life Prediction,” Knowledge-Based Systems, vol. 252, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Saleem Mohammad, and S.D. Sundarsingh Jeebaseelan, “Hybrid Sine Cosine and Spotted Hyena Based Chimp Optimization for PI Controller Tuning in Microgrids,” Scientific Reports, vol. 14, no. 1, pp. 1-29, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Masoud Nickparvar, Brain Tumor MRI Dataset, Kaggle. [Online]. Available: https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset
[33] Abdullah A. Asiri et al., “Optimized Brain Tumor Detection: A Dual-Module Approach for MRI Image Enhancement and Tumor Classification,” IEEE Access, vol. 12, pp. 42868-42887, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Karrar Neamah et al., “Brain Tumor Classification and Detection Based DL Models: A Systematic Review,” IEEE Access, vol. 12, pp. 2517-2542, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Seoyoung Yoon, “Brain Tumor Classification Using a Hybrid Ensemble of Xception and Parallel Deep CNN Models,” Informatics in Medicine Unlocked, vol. 54, pp. 1-8, 2025.
[CrossRef] [Google Scholar] [Publisher Link]