A non-invasive diagnostic approach for neuroblastoma utilizing preoperative enhanced computed tomography and deep learning techniques

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.