Attention-Guided Transfer Learning Framework for Four-Class Brain Tumor Classification Using MRI

Summary

This study presents a lightweight attention-based deep learning framework for four-class brain tumor classification from MRI images using transfer learning with MobileNetV2, EfficientNetV1, and Inception-ResNet-V2. The model achieves high classification accuracy with reduced computational complexity, supporting resource-constrained clinical decision-support applications.

Abstract

The objective of this research is to develop a lightweight yet accurate deep learning framework for classifying brain tumors into four categories—gliomas, meningiomas, pituitary tumors, and healthy brain tissue—using magnetic resonance imaging (MRI) data. The proposed methodology combines transfer learning with three pre-trained convolutional neural network models—MobileNetV2, EfficientNetV1, and Inception-ResNet-V2—integrated with an attention mechanism that emulates the human visual system’s ability to focus on clinically relevant regions of an image. This attention-guided approach enhances tumor localization while suppressing irrelevant background information. The framework is evaluated on a large, publicly available brain MRI dataset containing thousands of labeled images across the four classes. Standard preprocessing, data augmentation, and training protocols are employed to allow straightforward replication of the proposed method. Experimental results demonstrate that EfficientNetV1 achieves the highest classification performance, reaching superior accuracy compared to both MobileNetV2 and Inception-ResNet-V2. The study concludes that the proposed lightweight, attention-based framework effectively balances accuracy and computational complexity, making it highly suitable for real-time and mobile healthcare applications, particularly in resource-constrained environments.

Introduction

Accurate classification of brain tumors using magnetic resonance imaging (MRI) plays a critical role in clinical diagnosis, treatment planning, and prognosis estimation¹. Brain tumors, including glioma, meningioma, and pituitary tumors, exhibit distinct pathological characteristics and treatment responses; therefore, early and reliable identification is essential for improving therapeutic outcomes and reducing disease progression. MRI is widely preferred for brain tumor evaluation because of its superior soft-tissue contrast, noninvasive nature, and ability to visualize tumor morphology across multiple anatomical planes. However, manual interpr....

Protocol

This study used a publicly available, anonymized dataset; therefore, informed consent was not required. The proposed approach comprises six components: preprocessing, convolution module, attention module, integration of convolution and attention modules, and fully connected (FC) layers. Figure 1 illustrates the complete workflow of our method.

Dataset acquisition
Labeled brain MRI images are obtained from a publicly available Kaggle dataset comprising four tumor categories and normal tissue.

Data preprocessing
Images are resized to a unif....

Results

Three pre-trained deep learning models, namely MobileNetV2, EfficientNetV1, and Inception-ResNet-V2, were evaluated for four-class brain tumor classification using MRI images.

Dataset Used
The proposed framework was evaluated using a publicly available brain tumor MRI dataset obtained from the Kaggle platform. The dataset consisted of 835 normal brain MRI images, 826 glioma images, 822 meningioma images, and 827 pituitary tumor images. Representative MRI samples from the d.......

Discussion

The performance evaluation, based on accuracy, recall, F1-score, and precision, reveals that both MobileNetV2 and Inception-ResNet-V2 achieve comparable results in brain tumor classification, each attaining an aggregate and weighted-average accuracy of 97–98%. In contrast, EfficientNet outperforms the other models, achieving 99% accuracy across macro- and weighted-average metrics, thanks to its enhanced generalization and compound scaling strategy. These findings are consistent with previous studies demonstrating t.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Brain MRI Dataset	IEEE	https://dx.doi.org/10.21227/q2vt-tf46	The dataset is downloaded with institutite access. The MRI dataset contains four classes: glioma, meningioma, pituitary tumor, and normal brain
CNN Accelerator (Layer Fusion)	Proposed work	—	Logical fusion of top convolutional layers to reduce memory access and improve computational efficiency
Custom Attention Module	Proposed work	—	Attention mechanism integrating max-pooling and average-pooling to emphasize tumor-relevant regions
EfficientNetV1	Google	Pre-trained (ImageNet)	Scalable CNN architecture providing optimal accuracy–efficiency trade-off
Google Colaboratory	Google	https://colab.research.google.com/	Cloud-based environment used for model training, evaluation, and experimentation with GPU support
Inception-ResNet-V2	Google	Pre-trained (ImageNet)	Deep hybrid CNN architecture combining Inception modules with residual connections
Keras	Google	v2.6.0	High-level neural network API built on TensorFlow for rapid model prototyping
Matplotlib	Matplotlib Developers	v3.4.3	Visualization library used for plotting training curves, confusion matrices, and performance metrics
MobileNetV2	Google	Pre-trained (ImageNet)	Lightweight CNN backbone optimized for mobile and edge deployment
NumPy	NumPy Developers	v1.21.4	Core scientific computing library used for numerical operations
Pandas	Pandas Development Team	v1.3.4	Data handling and manipulation library used for dataset organization and metadata processing
Python	Python Software Foundation	v3.8.10	Primary programming language used for data preprocessing, model development, and evaluation
Scikit-learn	Scikit-learn Developers	Latest stable	Used for performance evaluation metrics such as precision, recall, F1-score, and confusion matrices
Seaborn	Seaborn Developers	Latest stable	High-level statistical visualization library used for enhanced graphical analysis
TensorFlow	Google	v2.6.0	End-to-end deep learning framework used for implementing CNN backbones and attention modules