Liu Wei, Guo Xingxin
School of Informatics, Hunan University of Chinese Medicine, Changsha, Hunan, China.
PLoS One. 2025 Jun 16;20(6):e0325483. doi: 10.1371/journal.pone.0325483. eCollection 2025.
Advancements in medical imaging technology have facilitated the acquisition of high-quality brain images through computed tomography (CT) or magnetic resonance imaging (MRI), enabling professional brain specialists to diagnose brain tumors more effectively. However, manual diagnosis is time-consuming, which has led to the growing importance of automatic detection and classification through brain imaging. Conventional object detection models for brain tumor detection face limitations in brain tumor detection owing to the significant differences between medical images and natural scene images, as well as challenges such as complex backgrounds, noise interference, and blurred boundaries between cancerous and normal tissues. This study investigates the application of deep learning to brain tumor detection, analyzing the effect of three factors, the number of model parameters, input data batch size, and the use of anchor boxes, on detection performance. Experimental results reveal that an excessive number of model parameters or the use of anchor boxes may reduce detection accuracy. However, increasing the number of brain tumor samples improves detection performance. This study, introduces a backbone network built using RepConv and RepC3, along with FGConcat feature map splicing module to optimize the brain tumor detection model. The experimental results show that the proposed RepConv-RepC3-FGConcat Network (RRFNet) can learn underlying semantic information about brain tumors during training stage, while maintaining a low number of parameters during inference, which improves the speed of brain tumor detection. Compared with YOLOv8, RRFNet achieved a higher accuracy in brain tumor detection, with a mAP value of 79.2%. This optimized approach enhances both accuracy and efficiency, which is essential in clinical settings where time and precision are critical.
医学成像技术的进步促进了通过计算机断层扫描（CT）或磁共振成像（MRI）获取高质量的脑部图像，使专业的脑科专家能够更有效地诊断脑肿瘤。然而，人工诊断耗时，这使得通过脑成像进行自动检测和分类的重要性日益凸显。用于脑肿瘤检测的传统目标检测模型在脑肿瘤检测中面临局限性，这是由于医学图像与自然场景图像存在显著差异，以及诸如复杂背景、噪声干扰和癌组织与正常组织之间边界模糊等挑战。本研究调查了深度学习在脑肿瘤检测中的应用，分析了模型参数数量、输入数据批次大小和锚框的使用这三个因素对检测性能的影响。实验结果表明，过多的模型参数或锚框的使用可能会降低检测准确率。然而，增加脑肿瘤样本数量可提高检测性能。本研究引入了一种使用RepConv和RepC3构建的骨干网络，以及FGConcat特征图拼接模块来优化脑肿瘤检测模型。实验结果表明，所提出的RepConv-RepC3-FGConcat网络（RRFNet）在训练阶段能够学习到关于脑肿瘤的潜在语义信息，同时在推理过程中保持较少的参数数量，从而提高了脑肿瘤检测的速度。与YOLOv8相比，RRFNet在脑肿瘤检测中实现了更高的准确率，平均精度均值（mAP）值为79.2%。这种优化方法提高了准确性和效率，这在时间和精度至关重要的临床环境中至关重要。
