Early detection of lung cancer is the major challenge for physicians to treat and control this deadly disease whose primary step is to detect pulmonary nodule from thoracic computed tomography (CT) images. In view of increasing the accuracy of the pulmonary nodule detection methodology, this paper proposes a novel technique that can aid early diagnosis of the patients. The study has considered high resolution computed tomography (HRCT) images from two public datasets LIDC and Lung-TIME and an independent dataset, created in collaboration between Peerless Hospital Kolkata and University of Calcutta. The key feature of the test dataset is that the class features are imbalanced in nature. The structures associated with lung parenchyma are segmented using parameterized multi-level thresholding technique, grayscale morphology, and rolling ball algorithm. Then random under sampling is implemented to overcome the imbalance class problem, followed by a feature selection methodology using binary particle swarm optimization (BPSO). The nodule and non-nodule classification are performed by implementing ensemble stacking. Indeed, it has been observed that there exists insufficient published literature that has been considered similar looking pulmonary abnormalities as non-nodule objects as well as imbalance class problem and feature selection algorithm to design an automated, accurate and robust model for automated detection of the pulmonary nodule. In reference to the LIDC dataset, the false positive, false negative detection rates and sensitivity are 1.01/scan, 0.56/scan and 99.01% respectively, which is an improvement in terms of accuracy as compared to the existing state-of-the-art research works.

肺癌的早期检测是医生治疗和控制这种致命疾病的主要挑战，而这是从胸腔计算机断层扫描（CT）图像中检测肺结节的主要步骤。鉴于提高肺结节检测方法的准确性，本文提出了一种可以帮助患者早期诊断的新技术。这项研究考虑了来自两个公共数据集LIDC和Lung-TIME以及一个独立数据集的高分辨率计算机断层扫描（HRCT）图像，该数据集是由Peerless Hospital Kolkata和University of Calcutta合作创建的。测试数据集的关键特征是类特征本质上是不平衡的。使用参数化多级阈值化技术，灰度形态学对与肺实质相关的结构进行分割，和滚球算法。然后实施随机欠采样以克服不平衡类问题，随后采用二进制粒子群算法（BPSO）进行特征选择。结节和非结节分类是通过实现集成堆叠来执行的。确实，已经观察到，没有足够的已发表文献被认为与非结节对象看起来相似的肺部异常以及失衡类别问题和特征选择算法无法设计出自动，准确和鲁棒的模型来自动检测肺结核。参考LIDC数据集，假阳性，假阴性检测率和灵敏度分别为1.01 / scan，0.56 / scan和99.01％，