BIoMT-ISeg: Blockchain internet of medical things for intelligent segmentation - Belhadi, Asma; Holland, Jon-Olav; Yazidi, Anis; Srivastava, Gautam; Lin, Jerry Chun-Wei; Djenouri, Youcef (Frontiers in Physiology, 2023)
In the quest of training complicated medical data for the 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 were used to optimize the hyper-parameters of the used architectures. The training process was also protected with the help of the blockchain. Finally, an ensemble learning system based on voting mechanism was developed to combine the local outputs of various segmentation models into a global output. Our method shows that good 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 reached 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%.