Objective In the field of brain tumor detection, fast and high-precision object detection models have important clinical value. However, as the complexity of medical imaging data continues to increase, so does the requirement for model detection accuracy and efficiency. Therefore, this paper aims to improve the existing RCS-YOLO model to further improve its detection performance and meet the requirements of medical image analysis for subtle feature detection.Methods In this paper, an improved RCS-YOLO model (MARCS-YOLO) is proposed, which integrates multiple attention mechanisms (including SE Attention, CBAM and GAM) in the RCS-OSA module and optimizes feature fusion strategies to realize the complementarity of attention mechanisms in different dimensions and significantly improve the detection performance of the model. In addition, this paper introduces a learnable channel shuffle mechanism to flexibly control the information flow between channels, avoid the limitations of fixed shuffle, and improve the robustness of feature expression.Results Experimental results on the Br35H dataset show that the improved model achieves 0.95, 0.955, 0.953 and 0.742 in terms of accuracy, recall, mAP50 and mAP50:95, respectively, which are improved compared with the original RCS-YOLO model. The improvement of these indicators shows that the improved model is able to identify brain tumor boundaries more accurately, especially in detecting small tumors.Conclusions The improved MARCS-YOLO model significantly improves the performance of brain tumor detection by integrating multiple attention mechanisms and optimizing feature fusion strategies, and meets the high requirements of medical image analysis for subtle feature detection. Experimental results show that the proposed model is better than the original model in key indicators such as accuracy, recall and mAP, showing its potential value in clinical application.