Alshamrani Khalaf, Alshamrani Hassan A
Radiology Sciences Department, College of Medical Sciences, Najran University, Najran, Saudi Arabia.
School of Medicine and Population Health, University of Sheffield, Sheffield, UK.
J Multidiscip Healthc. 2025 Jan 17;18:239-253. doi: 10.2147/JMDH.S472170. eCollection 2025.
This paper aimed to enhance the diagnostic process of lung abnormalities in computed tomography (CT) images, particularly in distinguishing cancer cells from normal chest tissue. The rapid and uneven growth of cancer cells, presenting with variable symptoms, necessitates an advanced approach for accurate identification.
To develop a dual-sampling network targeting lung infection regions to address the diagnostic challenge. The network was designed to adapt to the uneven distribution of infection areas, which could be predominantly minor or major in different regions.
A total of 150 CT images were analyzed using the dual-sampling network. Two sampling approaches were compared: the proposed dual-sampling technique and a uniform sampling method.
The dual-sampling network demonstrated superior performance in detecting lung abnormalities compared to uniform sampling. The uniform sampling method, the network results: an F1-Score of 94.2%, accuracy of 94.5%, sensitivity of 93.5%, specificity of 95.4%, and an area under the curve (AUC) of 98.4%. However, with the proposed dual-sampling method, the network reached an F1-score of 94.9%, accuracy of 95.2%, specificity of 96.1%, sensitivity of 94.2%, and an AUC of 95.5%.
This study suggests that the proposed dual-sampling network significantly improves the precision of lung abnormality diagnosis in CT images. This advancement has the potential to aid radiologists in making more accurate diagnoses, ultimately benefiting patient treatment and contributing to better overall population health. The efficiency and effectiveness of the dual-sampling approach in managing the uneven distribution of lung infection areas are key to its success.
本文旨在改进计算机断层扫描（CT）图像中肺部异常的诊断过程，特别是区分癌细胞与正常胸部组织。癌细胞生长迅速且不均匀，症状多样，因此需要先进的方法来进行准确识别。
开发一种针对肺部感染区域的双采样网络，以应对诊断挑战。该网络旨在适应感染区域分布不均的情况，不同区域的感染区域可能主要为小区域或大区域。
使用双采样网络分析了总共150张CT图像。比较了两种采样方法：提出的双采样技术和均匀采样方法。
与均匀采样相比，双采样网络在检测肺部异常方面表现出卓越性能。均匀采样方法的网络结果为：F1分数94.2%，准确率94.5%，灵敏度93.5%，特异性95.4%，曲线下面积（AUC）98.4%。然而，采用提出的双采样方法时，网络的F1分数达到94.9%，准确率95.2%，特异性96.1%，灵敏度94.2%，AUC为95.5%。
本研究表明，所提出的双采样网络显著提高了CT图像中肺部异常诊断的精度。这一进展有可能帮助放射科医生做出更准确的诊断，最终有益于患者治疗并促进总体人群健康状况的改善。双采样方法在处理肺部感染区域分布不均方面的效率和有效性是其成功的关键。
