Currently, lung cancer has become one of the most common and deadliest types of cancer. Due to its severity, many countries are now encouraging their at-risk citizens to test and treat lung cancer early. Lung cancer has been worse for poor regions or countries, whose citizens are more susceptible to lung cancer, as the local medical resources and healthcare provider level are inadequate. In recent years, this situation can be significantly improved by leveraging the existing datasets about lung cancer in developed countries. However, due to the poor synchronization of data collection methods, the collected data is heterogeneous, and can't be readily used. Artificial intelligence (AI), big data, cloud computing, and the internet of things accelerate the 4th revolution in the medical industry, and we called it medical industry 4.0. In the medical industry 4.0, lung cancer can be early detected by using a very intelligent approach. In this paper, using AI and cloud platform techniques in the medical industry 4.0, we propose an intelligent detection system including data integration, detection, historical cases comparison, similar cases inquiry, and retrieval for lung cancer. In this system, doctors can integrate the heterogeneous data at hand, and source a large amount of integrated data as a reference when treating the patient. A cloud-based deep learning model is integrated into this system, and then a content-based image retrieval system for similarity comparison is used. Finally, some public datasets are used to train and test this system, and results prove its performance is better than that of some baseline approaches. Then the similar case finding is evaluated with cosine similarity and all similarities reach over 0.93. The heterogeneous data integration system creates a good effect in helping doctors and patients access better diagnosis and treatment for lung cancer.

目前，肺癌已成为最常见和最致命的癌症类型之一。由于其严重性，许多国家现在鼓励其高危公民及早检测和治疗肺癌。由于当地医疗资源和医疗保健提供者水平不足，其公民更容易患肺癌的贫困地区或国家的肺癌更严重。近年来，通过利用发达国家现有的肺癌数据集，可以显着改善这种情况。但是，由于数据采集方式的同步性较差，采集到的数据是异构的，不能方便地使用。人工智能（AI）、大数据、云计算等物联网加速了医疗行业的第四次革命，我们称之为医疗工业4.0。在医疗工业 4.0 中，可以通过非常智能的方法对肺癌进行早期检测。本文利用医疗工业4.0的人工智能和云平台技术，提出了肺癌的数据整合、检测、历史病例比对、相似病例查询、检索等智能检测系统。在该系统中，医生可以整合手头的异构数据，并在治疗患者时获取大量整合数据作为参考。基于云的将深度学习模型集成到该系统中，然后使用基于内容的图像检索系统进行相似度比较。最后，使用一些公共数据集来训练和测试这个系统，结果证明它的性能优于一些基线方法。然后用余弦相似度评估相似案例发现，所有相似度均超过 0.93。异构数据集成系统在帮助医生和患者获得更好的肺癌诊断和治疗方面创造了良好的效果。