ESA-YOLOv5m: A Lightweight Spatial and Improved Attention-Driven Detection for Brain Tumor MRI Analysis

Maram Fahaad Almufareh^1*Noshina Tariq²Mamoona Humayun^3*Haya Aldossary⁴Meshal Alharbi⁵

• ¹Jouf University Computer and Information Sciences, Sakaka, Saudi Arabia
• ²National University of Computer and Emerging Sciences, Islamabad, Pakistan
• ³University of Roehampton London, Roehampton, United Kingdom
• ⁴Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
• ⁵Prince Sattam bin Abdulaziz University, Al Kharj, Saudi Arabia

The early and accurate detection of brain tumors is vital for improving patient outcomes, enabling timely clinical interventions, and reducing diagnostic uncertainty. Despite advances in deep learning, conventional Convolutional Neural Network (CNN)-based models often struggle with small or low-contrast tumors. They also remain computationally demanding for real-time clinical deployment. This study presents an Enhanced Spatial Attention (ESA)-integrated You Only Look Once v5 medium (YOLOv5m) architecture, a lightweight and efficient framework for brain tumor detection in MRI scans. The ESA module, positioned after the Spatial Pyramid Pooling-Fast (SPPF) layer, enhances feature discrimination by emphasizing diagnostically relevant regions while suppressing background noise, thereby improving localization accuracy without increasing computational complexity. Experiments were conducted on the Figshare brain tumor MRI dataset containing three tumor classes: glioma, meningioma, and pituitary. ESA-YOLOv5m achieved a Precision of 90%, Recall of 90%, and mean Average Precision (mAP)@0.5 of 91%, surpassing the baseline YOLOv5m by approximately 11-12%. An ablation study further confirmed that placing the ESA module after the SPPF layer yields the highest performance (mAP@0.5 = 0.91), while earlier integration produced marginally lower results. Class-wise analyses demonstrated consistent gains (mAP range 0.87-0.98), and five-fold cross-validation showed stable performance (mAP@0.5 = 0.910 ± 0.006). Efficiency tests revealed negligible overhead, with less than a Sample et al. ESA-YOLOv5m 4.3% increase in parameters and an average latency below 10 ms per image. Overall, the results validate that integrating a lightweight spatial attention mechanism significantly enhances tumor localization and model generalization while preserving real-time inference. The proposed ESA-YOLOv5m framework provides a reliable and scalable solution for automated brain tumor detection, suitable for clinical decision-support systems and edge healthcare applications.