Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan.
Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Republic of Korea.
Sensors (Basel). 2023 Jan 9;23(2):743. doi: 10.3390/s23020743.
Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety, and it is anticipated that deep learning (DL) will be the most effective way of detecting COVID-19 and other chest diseases such as lung cancer (LC), tuberculosis (TB), pneumothorax (PneuTh), and pneumonia (Pneu). However, data sharing across hospitals is hampered by patients' right to privacy, leading to unexpected results from deep neural network (DNN) models. Federated learning (FL) is a game-changing concept since it allows clients to train models together without sharing their source data with anybody else. Few studies, however, focus on improving the model's accuracy and stability, whereas most existing FL-based COVID-19 detection techniques aim to maximize secondary objectives such as latency, energy usage, and privacy. In this work, we design a novel model named decision-making-based federated learning network (DMFL_Net) for medical diagnostic image analysis to distinguish COVID-19 from four distinct chest disorders including LC, TB, PneuTh, and Pneu. The DMFL_Net model that has been suggested gathers data from a variety of hospitals, constructs the model using the DenseNet-169, and produces accurate predictions from information that is kept secure and only released to authorized individuals. Extensive experiments were carried out with chest X-rays (CXR), and the performance of the proposed model was compared with two transfer learning (TL) models, i.e., VGG-19 and VGG-16 in terms of accuracy (ACC), precision (PRE), recall (REC), specificity (SPF), and F1-measure. Additionally, the DMFL_Net model is also compared with the default FL configurations. The proposed DMFL_Net + DenseNet-169 model achieves an accuracy of 98.45% and outperforms other approaches in classifying COVID-19 from four chest diseases and successfully protects the privacy of the data among diverse clients.
新型冠状病毒肺炎（COVID-19）仍然是对全球健康和安全的威胁，预计深度学习（DL）将是检测 COVID-19 以及其他胸部疾病（如肺癌（LC）、肺结核（TB）、气胸（PneuTh）和肺炎（Pneu））的最有效方法。然而，由于患者的隐私权，医院之间的数据共享受到阻碍，导致深度神经网络（DNN）模型产生意外结果。联邦学习（FL）是一个改变游戏规则的概念，因为它允许客户端在不与其他任何人共享其源数据的情况下一起训练模型。然而，很少有研究关注提高模型的准确性和稳定性，而大多数现有的基于 FL 的 COVID-19 检测技术旨在最大限度地提高次要目标，如延迟、能源使用和隐私。在这项工作中，我们设计了一种名为基于决策的联邦学习网络（DMFL_Net）的新型模型，用于医学诊断图像分析，以区分 COVID-19 与包括 LC、TB、PneuTh 和 Pneu 在内的四种不同的胸部疾病。所提出的 DMFL_Net 模型从各种医院收集数据，使用 DenseNet-169 构建模型，并仅向授权人员发布安全保护的信息以做出准确预测。我们使用胸部 X 射线（CXR）进行了广泛的实验，并将所提出的模型与两种迁移学习（TL）模型（即 VGG-19 和 VGG-16）在准确性（ACC）、精度（PRE）、召回率（REC）、特异性（SPF）和 F1 度量方面进行了比较。此外，还将 DMFL_Net 模型与默认的 FL 配置进行了比较。所提出的 DMFL_Net + DenseNet-169 模型在区分 COVID-19 和四种胸部疾病方面达到了 98.45%的准确率，并且在保护不同客户端之间的数据隐私方面表现出色。
