Hyperparameters of the base classifiers.

Lung cancer is one of the leading causes of cancer-related deaths worldwide. To reduce the mortality rate, early detection and proper treatment should be ensured. Computer-aided diagnosis methods analyze different modalities of medical images to increase diagnostic precision. In this paper, we propose an ensemble model, called the Mitscherlich function-based Ensemble Network (MENet), which combines the prediction probabilities obtained from three deep learning models, namely Xception, InceptionResNetV2, and MobileNetV2, to improve the accuracy of a lung cancer prediction model. The ensemble approach is based on the Mitscherlich function, which produces a fuzzy rank to combine the outputs of the said base classifiers. The proposed method is trained and tested on the two publicly available lung cancer datasets, namely Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) and LIDC-IDRI, both of these are computed tomography (CT) scan datasets. The obtained results in terms of some standard metrics show that the proposed method performs better than state-of-the-art methods. The codes for the proposed work are available at https://github.com/SuryaMajumder/MENet.肺癌是全球范围内引发癌症相关死亡的主要病因之一。为降低肺癌相关死亡率，需保障早期筛查与规范治疗的有效落实。计算机辅助诊断（Computer-aided diagnosis, CAD）技术通过分析多模态医学影像以提升诊断精准度。本文提出一种基于米切利希函数的集成网络（Mitscherlich function-based Ensemble Network，简称MENet），该模型融合Xception、InceptionResNetV2与MobileNetV2三种深度学习模型输出的预测概率，旨在提升肺癌预测模型的准确率。该集成方案以米切利希函数为核心，通过生成模糊秩来整合上述基础分类器的输出结果。本文所提方法在两款公开肺癌数据集上完成训练与测试，分别为伊拉克肿瘤教学医院/国家癌症疾病中心（Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases，简称IQ-OTH/NCCD）数据集与LIDC-IDRI数据集，二者均为计算机断层扫描（Computed Tomography, CT）影像数据集。基于多项标准评测指标的实验结果表明，本文所提方法的性能优于当前最先进的同类方法。本研究相关代码已开源至：https://github.com/SuryaMajumder/MENet。