Wang Yuanyuan, Wang Fangfang, Qin Zixin, Fu Yongcheng, Wang Jingyue, Li Shangkun, Zhang Da
Department of Pediatric Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450000, China.
Department of Electronic information, Zhengzhou University Cyberspace Security College, Zhengzhou450000, Henan, China.
Sci Rep. 2025 Apr 26;15(1):14652. doi: 10.1038/s41598-025-99451-5.
Neuroblastoma presents a wide variety of clinical phenotypes, demonstrating different levels of benignity and malignancy among its subtypes. Early diagnosis is essential for effective patient management. Computed tomography (CT) serves as a significant diagnostic tool for neuroblastoma, utilizing machine vision imaging, which offers advantages over traditional X-ray and ultrasound imaging modalities. However, the high degree of similarity among neuroblastoma subtypes complicates the diagnostic process. In response to these challenges, this study presents a modified version of the You Only Look Once (YOLO) algorithm, called YOLOv8-IE. This revised approach integrates feature fusion and inverse residual attention mechanisms. The aim of YOLO-IE is to improve the detection and classification of neuroblastoma tumors. In light of the image features, we have implemented the inverse residual-based attention structure (iRMB) within the detection network of YOLOv8, thereby enhancing the model's ability to focus on significant features present in the images. Additionally, we have incorporated the centered feature pyramid EVC module. Experimental results show that the proposed detection network, named YOLO-IE, attains a mean Average Precision (mAP) 7.9% higher than the baseline model, YOLO. The individual contributions of iRMB and EVC to the performance improvement are 0.8% and 3.6% above the baseline model, respectively. This study represents a significant advancement in the field, as it not only facilitates the detection and classification of neuroblastoma but also demonstrates the considerable potential of machine learning and artificial intelligence in the realm of medical diagnosis.
神经母细胞瘤呈现出多种多样的临床表型，在其亚型中表现出不同程度的良性和恶性。早期诊断对于有效的患者管理至关重要。计算机断层扫描（CT）作为神经母细胞瘤的重要诊断工具，利用机器视觉成像，与传统的X射线和超声成像方式相比具有优势。然而，神经母细胞瘤亚型之间的高度相似性使诊断过程变得复杂。针对这些挑战，本研究提出了一种名为YOLOv8-IE的You Only Look Once（YOLO）算法的改进版本。这种改进方法集成了特征融合和逆残差注意力机制。YOLO-IE的目的是提高神经母细胞瘤肿瘤的检测和分类能力。根据图像特征，我们在YOLOv8的检测网络中实现了基于逆残差的注意力结构（iRMB），从而增强了模型聚焦于图像中显著特征的能力。此外，我们还纳入了中心特征金字塔EVC模块。实验结果表明，所提出的名为YOLO-IE的检测网络的平均平均精度（mAP）比基线模型YOLO高7.9%。iRMB和EVC对性能提升的个体贡献分别比基线模型高出0.8%和3.6%。这项研究代表了该领域的一项重大进展，因为它不仅促进了神经母细胞瘤的检测和分类，还展示了机器学习和人工智能在医学诊断领域的巨大潜力。
