The presentation of the COVID19 has endangered several million lives worldwide causing thousands of deaths every day. Evolution of COVID19 as a pandemic calls for automated solutions for initial screening and treatment management. In addition to the thermal scanning mechanisms, findings from chest X-ray imaging examinations are reliable predictors in COVID19 detection, long-term monitoring and severity evaluation. This paper presents a novel deep transfer learning based framework for COVID19 detection and segmentation of infections from chest X-ray images. It is realized as a two-stage cascaded framework with classifier and segmentation subnetwork models. The classifier is modeled as a fine-tuned residual SqueezeNet network, and the segmentation network is implemented as a fine-tuned SegNet semantic segmentation network. The segmentation task is enhanced with a bioinspired Gaussian Mixture Model-based super pixel segmentation. This framework is trained and tested with two public datasets for binary and multiclass classifications and infection segmentation. It achieves accuracies of 99.69% and 99.48% for binary and three class classifications, and a mean accuracy of 83.437% for segmentation. Experimental results and comparative evaluations demonstrate the superiority of this unified model and signify potential extensions for biomarker definition and severity quantization.
Smart healthcare systems have been widely applied in the fields of intelligent healthcare, self-monitoring, diagnosis, and emergency. In recent years, there have been growing concerns regarding the privacy of the data collected from the users of the smart healthcare systems. This chapter proposes a light-weight federated learning framework based on multi-key homomorphic encryption for deploying predictive models trained on patient data distributed across multiple healthcare institutions without exchanging them. Two predictive models based on the proposed framework are deployed for in-house mortality prediction from patient data and COVID-19 detection from chest x-ray images. Performance evaluation of these models with standard datasets and comparative analyses show that the proposed models are superior to state-of-the-art approaches. The proposed framework and the models are potential solutions to improve the quality of healthcare across multiple healthcare institutions, protecting the sensitive patient data and ensuring personalization of healthcare.
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