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Go to Editorial ManagerFederated learning (FL) is one of the newest and most significant fields for developing artificial intelligence applications. This technology trains its models in a distributed way, using data from different clients who work together in the system without sharing their data. The training process is kept local to protect the privacy of the data. Among the many difficulties that have arisen due to the novelty of this technology is the issue of heterogeneous data between typical clients. Client’s data may differ from each other in different respects, for example non identically and independent distribution (non-IID) between clients and the difference in the type of data used in each client. This can lead to inconsistencies in the model’s predictions and other undesirable outcomes. This paper discussed ways to solve this problem where clients with heterogeneous data were dealt with in terms of number and type. Because there are different types of image data through which doctors can diagnose coronavirus, such as x-rays, CT-scan. A hybrid convolution neural network (CNN ) and long short-term memory model (LSTM) has been proposed in a federated learning system to predict the incidence of this disease by using two clients, each with one of these different data. Good results were obtained with an accuracy of more than 99% in one customer and more than 95% in the second client while maintaining the privacy of this data.
Brain tumors are collections of abnormal tissues within the brain. The regular function of the brain may be affected as it grows within the region of the skull. Brain tumors are critical for improving treatment options and patient survival rates to prevent and treat them. The diagnosis of cancer utilizing manual approaches for numerous magnetic resonance imaging (MRI) images is the most complex and time-consuming task. Brain tumor segmentation must be carried out automatically. A proposed strategy for brain tumor segmentation is developed in this paper. For this purpose, images are segmented based on region-based and edge-based. Brain tumor segmentation 2020 (BraTS2020) dataset is utilized in this study. A comparative analysis of the segmentation of images using the edge-based and region-based approach with U-Net with ResNet50 encoder, architecture is performed. The edge-based segmentation model performed better in all performance metrics compared to the region-based segmentation model and the edge-based model achieved the dice loss score of 0. 008768, IoU score of 0. 7542, f1 score of 0. 9870, the accuracy of 0. 9935, the precision of 0. 9852, recall of 0. 9888, and specificity of 0. 9951.