Belhadi Asma, Holland Jon-Olav, Yazidi Anis, Srivastava Gautam, Lin Jerry Chun-Wei, Djenouri Youcef
School of Economics, Innovation and Technology, Kristiania University College, Oslo, Norway.
Department of Computer Science, OsloMet, Oslo, Norway.
Front Physiol. 2023 Jan 12;13:1097204. doi: 10.3389/fphys.2022.1097204. eCollection 2022.
In the quest of training complicated medical data for Internet of Medical Things (IoMT) scenarios, this study develops an end-to-end intelligent framework that incorporates ensemble learning, genetic algorithms, blockchain technology, and various U-Net based architectures. Genetic algorithms are used to optimize the hyper-parameters of the used architectures. The training process was also protected with the help of blockchain technology. Finally, an ensemble learning system based on voting mechanism was developed to combine local outputs of various segmentation models into a global output. Our method shows that strong performance in a condensed number of epochs may be achieved with a high learning rate and a small batch size. As a result, we are able to perform better than standard solutions for well-known medical databases. In fact, the proposed solution reaches 95% of intersection over the union, compared to the baseline solutions where they are below 80%. Moreover, with the proposed blockchain strategy, the detected attacks reached 76%.
在为医疗物联网（IoMT）场景训练复杂医学数据的过程中，本研究开发了一个端到端智能框架，该框架融合了集成学习、遗传算法、区块链技术以及各种基于U-Net的架构。遗传算法用于优化所用架构的超参数。训练过程也借助区块链技术得到了保护。最后，开发了一种基于投票机制的集成学习系统，将各种分割模型的局部输出组合成全局输出。我们的方法表明，通过高学习率和小批量大小，在较少的轮次中就能实现强大的性能。因此，我们在知名医学数据库上的表现优于标准解决方案。事实上，与低于80%的基线解决方案相比，所提出的解决方案达到了95%的交并比。此外，通过所提出的区块链策略，检测到的攻击达到了76%。
