Multi-Path Convolutional Architecture with Channel-Wise Attention for Multiclass Brain Tumor Detection in Magnetic Resonance Imaging Scans

Accurately detecting and classifying brain tumors in magnetic resonance imaging (MRI) scans poses formidable challenges, stemming from the heterogeneous presentation of tumors and the need for reliable, real-time diagnostic outputs. In this paper, we propose a novel multi-path convolutional architecture enhanced with channel-wise attention mechanisms, evaluated on a comprehensive four-class brain tumor dataset. Specifically: (i) we design a parallel feature extraction strategy that captures nuanced tumor morphologies, while channel-wise attention refines salient characteristics; (ii) we employ systematic data augmentation, yielding a balanced dataset of 6380 MRI scans to bolster model generalization; (iii) we compare the proposed architecture against state-of-the-art models, demonstrating superior diagnostic performance with 97.52% accuracy, 97.63% precision, 97.18% recall, 98.32% specificity, and an F1-score of 97.36%; and (iv) we report an inference speed of 5.13 ms per scan, alongside a higher memory footprint of approximately 26 GB, underscoring both the feasibility for real-time clinical application and the importance of resource considerations. These findings collectively highlight the proposed framework’s potential for improving automated brain tumor detection workflows and prompt further optimization for broader clinical deployment.