Abdul Sattar Shaikh Abdullah, Bhargavi M S, Kumar C Pavan
Department of Computer Science and Engineering, Bangalore Institute of Technology, Bangalore, 560004, Karnataka, India.
Department of Computer Science and Engineering, Indian Institute of Information Technology Dharwad, Dharwad, 580009, Karnataka, India.
Comput Methods Programs Biomed. 2023 Dec;242:107821. doi: 10.1016/j.cmpb.2023.107821. Epub 2023 Sep 21.
Background and Objective Respiratory Diseases are one of the leading chronic illnesses in the world according to the reports by World Health Organization. Diagnosing these respiratory diseases is done through auscultation where a medical professional listens to sounds of air in the lungs for anomalies through a stethoscope. This method necessitates extensive experience and can also be misinterpreted by the medical professional. To address this issue, we introduce an AI-based solution that listens to the lung sounds and classifies the respiratory disease detected. Since the research work deals with medical data that is tightly under wraps due to privacy concerns in the medical field, we introduce a Deep learning solution to classify the diseases and a custom Federated learning (FL) approach to further improve the accuracy of the deep learning model and simultaneously maintain data privacy. Federated Learning architecture maintains data privacy and facilitates a distributed learning system for medical infrastructures. Methods The approach utilizes Generative Adversarial Networks (GAN) based Federated learning approach to ensure data privacy. Generative Adversarial Networks generate new data by synthesizing new lung sounds. This new synthesized data is then converted to spectrograms and trained on a neural network to classify four lung diseases, Heart Attack and Normal breathing patterns. Furthermore, to address performance loss during FL, we also propose a new "Weighted Aggregation through Probability-based Ranking (FedWAPR)" algorithm for optimizing the FL aggregation process. The FedWAPR aggregation takes inspiration from exponential distribution function and ranks better performing clients according to it. Results and Conclusion A test accuracy of about 92% was achieved by the trained model while classifying various respiratory diseases and heart failure. Additionally, we developed a novel FedWAPR approach that significantly outperformed the FedAVG approach for the FL aggregate function. A patient can be checked for respiratory diseases using this improved learning approach without the need for extensive sensitive data recording or for making sure the data sample obtained is secure. In a decentralized training runtime, the trained model successfully classifies various respiratory diseases and heart failure using lung sounds with a test accuracy on par with a centralized model.
背景与目的 根据世界卫生组织的报告，呼吸系统疾病是全球主要的慢性疾病之一。这些呼吸系统疾病的诊断是通过听诊进行的，即医学专业人员通过听诊器倾听肺部空气声音以发现异常。这种方法需要丰富的经验，并且医学专业人员也可能会出现误判。为了解决这个问题，我们引入了一种基于人工智能的解决方案，该方案可以倾听肺部声音并对检测到的呼吸系统疾病进行分类。由于该研究工作涉及因医学领域隐私问题而严格保密的医学数据，我们引入了一种深度学习解决方案来对疾病进行分类，并采用一种定制的联邦学习（FL）方法来进一步提高深度学习模型的准确性，同时维护数据隐私。联邦学习架构可维护数据隐私，并为医疗基础设施提供分布式学习系统。
方法 该方法利用基于生成对抗网络（GAN）的联邦学习方法来确保数据隐私。生成对抗网络通过合成新的肺部声音来生成新数据。然后将这些新合成的数据转换为频谱图，并在神经网络上进行训练，以对四种肺部疾病、心脏病发作和正常呼吸模式进行分类。此外，为了解决联邦学习期间的性能损失问题，我们还提出了一种新的“基于概率排名的加权聚合（FedWAPR）”算法，用于优化联邦学习聚合过程。FedWAPR聚合借鉴了指数分布函数的思想，并据此对性能更好的客户端进行排名。
结果与结论 训练后的模型在对各种呼吸系统疾病和心力衰竭进行分类时，测试准确率约为92%。此外，我们开发了一种新颖的FedWAPR方法，该方法在联邦学习聚合函数方面明显优于FedAVG方法。使用这种改进的学习方法，可以在无需大量敏感数据记录或确保所获取的数据样本安全的情况下，对患者进行呼吸系统疾病检查。在分散式训练运行时，训练后的模型使用肺部声音成功地对各种呼吸系统疾病和心力衰竭进行了分类，测试准确率与集中式模型相当。
