Identifying the extent of brain tumors is a significant challenge in brain cancer treatment. The main difficulty is in the approximate detection of tumor size. Magnetic resonance imaging (MRI) has become a critical diagnostic tool. However, manually detecting the boundaries of brain tumors from MRI scans is a labor-intensive task that requires extensive expertise. Deep learning and computer-aided detection techniques have led to notable advances in machine learning for this purpose. In this paper, we propose a modified You Only Look Once (YOLOv8) model to accurately detect the tumors within the MRI images. The proposed model replaced the Non-Maximum Suppression (NMS) algorithm with a Real-Time Detection Transformer (RT- DETR) in the detection head. NMS filters out redundant or overlapping bounding boxes in the detected tumors, but they are hand-designed and pre-set. RT-DETR removes hand-designed components. The second improvement was made by replacing the normal convolution block with ghost convolution. Ghost Convolution reduces computational and memory costs while maintaining high accuracy and enabling faster inference, making it ideal for resource-constrained environments and real-time applications. The third improvement was made by introducing a vision transformer block in the backbone of YOLOv8 to extract context-aware features. We used a publicly available dataset of brain tumors in the proposed model. The proposed model performed better than the original YOLOv8 model and also performed better than other object detectors (Faster R- CNN, Mask R-CNN, YOLO, YOLOv3, YOLOv4, YOLOv5, SSD, RetinaNet, EfficientDet, and DETR). The proposed model achieved 0.91 mAP (mean Average Precision)@0.5.

翻译：脑肿瘤范围的界定是脑癌治疗中的重大挑战，主要难点在于肿瘤尺寸的近似检测。磁共振成像已成为关键诊断工具，但人工从MRI扫描中检测脑肿瘤边界是一项劳动密集型任务，需要大量专业知识。深度学习和计算机辅助检测技术为此推动了机器学习的显著进展。本文提出一种改进的You Only Look Once模型，用于精确检测MRI图像中的肿瘤。该模型在检测头中用实时检测变换器替代了非极大值抑制算法。NMS用于滤除检测肿瘤中冗余或重叠的边界框，但其属于人工设计且参数预设。RT-DETR则消除了人工设计组件。第二项改进是用Ghost卷积替代常规卷积块，该技术在保持高精度的同时降低计算与内存成本，实现更快推理，适用于资源受限环境和实时应用。第三项改进是在YOLOv8骨干网络中引入视觉变换器模块以提取上下文感知特征。我们在所提模型中使用了公开的脑肿瘤数据集。实验表明，改进模型性能优于原始YOLOv8模型，也优于其他目标检测器。该模型取得了0.91 mAP@0.5的检测精度。