计算技术的进步导致肺癌筛查转向自动化检测过程，特别是通过结节分割技术。这些技术采用阈值来区分软组织和硬组织，包括癌结节。准确检测靠近血管、支气管和胸膜等关键肺部结构的结节所面临的挑战凸显了采用更复杂的方法来提高诊断准确性的必要性。本文提出了在使用改进的卷积神经网络（CNN）作为分类器之前进行数据准备的组合处理滤波器。通过精细的过滤器，结节目标是固体、半固体和磨玻璃，范围从低阶段癌症（癌症筛查数据）到高阶段癌症。此外，还添加了两项额外的工作来解决胸膜旁结节，同时预处理和分类是在 3 维域中完成的，这与通常的图像分类相反。准确率输出表明，即使使用简单的分割网络，如果修改正确，也可以比其他八个模型提高分类结果。所提出的序列总准确度达到99.7%，其中癌症分类准确度为99.71%，非癌症准确度为99.82%，远高于之前的任何研究，这可以提高放射科医生的检测力度。

Advancements in computational technology have led to a shift towards automated detection processes in lung cancer screening, particularly through nodule segmentation techniques. These techniques employ thresholding to distinguish between soft and firm tissues, including cancerous nodules. The challenge of accurately detecting nodules close to critical lung structures such as blood vessels, bronchi, and the pleura highlights the necessity for more sophisticated methods to enhance diagnostic accuracy. This paper proposed combined processing filters for data preparation before using one of the modified Convolutional Neural Networks (CNN) as the classifier. With refined filters, the nodule targets are solid, semi-solid, and ground glass, ranging from low-stage cancer (cancer screening data) to high-stage cancer. Furthermore, two additional works were added to address juxta-pleural nodules while the pre-processing end and classification are done in a 3-dimensional domain in opposition to the usual image classification. The accuracy output indicates that even using a simple Segmentation Network if modified correctly, can improve the classification result compared to the other eight models. The proposed sequence total accuracy reached 99.7%, with 99.71% cancer class accuracy and 99.82% non-cancer accuracy, much higher than any previous research, which can improve the detection efforts of the radiologist.