An Attention-Enhanced CNN–Transformer Model for Brain Tumor Identification from MRI Scan

Abstract

Magnetic resonance imaging (MRI) is very important for clinical diagnosis and treatment planning, because it can accurately detect and classify brain tumors. Traditional Convolutional Neural Network (CNN) models can extract local features, but they are not able to get long-range contextual information from complex medical images. The proposed system is to develop a CNN-Transformer based framework for automatic brain tumor detection and multi-class classification. The model combines Channel Attention Networks (CAN) with Vision Transformer based models (ViT)/Dual Vision Transformer(DaViT). Due to which the CAN enhances feature learning by spotlighting tumor-related channels. Also the model is validated on BraTS MRI dataset with four classes, i.e. pituitary tumor, meningioma, glioma and normal cases. The results obtained based on CNN-Transformer gives a higher accuracy of 97.6% and higher precision of 0.96 that enables the transformer module to capture global spatial relationships within the image.