Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea.
Artif Intell Med. 2023 Jul;141:102572. doi: 10.1016/j.artmed.2023.102572. Epub 2023 May 4.
With an estimated five million fatal cases each year, lung cancer is one of the significant causes of death worldwide. Lung diseases can be diagnosed with a Computed Tomography (CT) scan. The scarcity and trustworthiness of human eyes is the fundamental issue in diagnosing lung cancer patients. The main goal of this study is to detect malignant lung nodules in a CT scan of the lungs and categorize lung cancer according to severity. In this work, cutting-edge Deep Learning (DL) algorithms were used to detect the location of cancerous nodules. Also, the real-life issue is sharing data with hospitals around the world while bearing in mind the organizations' privacy issues. Besides, the main problems for training a global DL model are creating a collaborative model and maintaining privacy. This study presented an approach that takes a modest amount of data from multiple hospitals and uses blockchain-based Federated Learning (FL) to train a global DL model. The data were authenticated using blockchain technology, and FL trained the model internationally while maintaining the organization's anonymity. First, we presented a data normalization approach that addresses the variability of data obtained from various institutions using various CT scanners. Furthermore, using a CapsNets method, we classified lung cancer patients in local mode. Finally, we devised a way to train a global model cooperatively utilizing blockchain technology and FL while maintaining anonymity. We also gathered data from real-life lung cancer patients for testing purposes. The suggested method was trained and tested on the Cancer Imaging Archive (CIA) dataset, Kaggle Data Science Bowl (KDSB), LUNA 16, and the local dataset. Finally, we performed extensive experiments with Python and its well-known libraries, such as Scikit-Learn and TensorFlow, to evaluate the suggested method. The findings showed that the method effectively detects lung cancer patients. The technique delivered 99.69 % accuracy with the smallest possible categorization error.
每年估计有 500 万人死于肺癌，是全球主要的致死原因之一。肺癌可以通过计算机断层扫描（CT）进行诊断。在诊断肺癌患者时，稀缺且值得信赖的人眼是一个基本问题。本研究的主要目的是在肺部 CT 扫描中检测恶性肺结节，并根据严重程度对肺癌进行分类。在这项工作中，使用了先进的深度学习（DL）算法来检测癌症结节的位置。此外，在考虑到组织的隐私问题的同时，与世界各地的医院共享数据也是一个现实生活中的问题。此外，训练全球 DL 模型的主要问题是创建协作模型和维护隐私。本研究提出了一种方法，该方法从多家医院获取少量数据，并使用基于区块链的联邦学习（FL）来训练全球 DL 模型。使用区块链技术对数据进行认证，FL 在国际上训练模型，同时保持组织的匿名性。首先，我们提出了一种数据归一化方法，用于解决从不同机构使用不同 CT 扫描仪获得的数据的可变性问题。此外，我们使用 CapsNets 方法在本地模式下对肺癌患者进行分类。最后，我们设计了一种使用区块链技术和 FL 协作训练全局模型的方法，同时保持匿名性。我们还从真实的肺癌患者那里收集数据进行测试。该方法在癌症成像档案（CIA）数据集、Kaggle 数据科学碗（KDSB）、LUNA 16 和本地数据集上进行了训练和测试。最后，我们使用 Python 及其著名的库（如 Scikit-Learn 和 TensorFlow）进行了广泛的实验，以评估所提出的方法。结果表明，该方法可以有效地检测肺癌患者。该技术以最小的分类错误实现了 99.69%的准确率。
