Privacy-preserving AI for early diagnosis of thoracic diseases using IoTs: A federated learning approach with multi-headed self-attention for facilitating cross-institutional study
IF 6 3区 计算机科学Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
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引用次数: 0
Abstract
Our study recognized the crucial role of early diagnosis of pulmonary radiological abnormalities such as pneumothorax, effusion, pneumonia, cardiomegaly, and COVID-19. We proposed FedXNet, which is a collaborative deep learning model based on federated learning (FL) exploiting edge computing resources efficiently to accurately deal with them and ensure privacy. Our developed model is notable for its integration of Multi-Headed Self-Attention, a complex technique that allows the model to focus on several parts of the input data at once. This improves the model’s capacity to uncover complex patterns and correlations within the medical images. This multi-class CNN system uses a thorough four-pronged approach: (1) facilitating cross-institutional, federated training without sacrificing the integrity of individual data, (2) image preprocessing to achieve robust model accuracy, (3) efficient Feature extraction using pre-trained models and our dedicated FedXNet architecture, as well as (4) a variety of classifiers tailored to each disease, resulting in impressive diagnostic performance for a range of thoracic diseases, including COVID-19. This model paves the way for a future where timely diagnosis and better patient outcomes become a reality, empowered by the collaborative spirit of FL exploiting edge computing resources of IoT for implementing robust deep learning models.
期刊介绍:
Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT.
The journal will place a high priority on timely publication, and provide a home for high quality.
Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.