Attention-Guided Transfer Learning Framework for Four-Class Brain Tumor Classification Using MRI.
Authors
Affiliations (6)
Affiliations (6)
- Department of Computer Science and Engineering, Sri Chaitanya Institute of Technology & Research; [email protected].
- Department of Computer Science and Engineering, Sri Chaitanya Institute of Technology & Research.
- Department of Artificial Intelligence and Data Science, JB Institute of Engineering and Technology.
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation (KLEF).
- Department of Electronics and Communication Engineering, Sri Mittapalli College of Engineering.
- School of Electronics Engineering (SENSE), VIT-AP University.
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.