Distributed edge to cloud ensemble deep learning architecture to diagnose Covid-19 from lung image in IoT based e-Health system

Today, with the expansion of technology and new architectures of deep learning, the accuracy of artificial intelligence methods in diagnosing diseases has increased. On the other hand, with the spread of new pandemic diseases such as Covid-19, timely and accurate diagnosis of the disease has become more important. Recently, proposed deep learning methods diagnose Covid-19 with acceptable accuracy but have expensive computational cost which could not distributed and implemented in edge devices. Sometimes the type of disease could be diagnosed by small models with few parameters. These small models can be placed in the fog or edge devices, and if they detect the disease with high confidence locally, the disease investigation request will not be sent to the cloud where the comprehensive and main trained model is located. Based on this idea; we proposed an ensemble of two deep learning models using boosting Shema named mobile COVID-Net, first a light weight MobileNet model designed and embedded in fog devices to diagnose pneumonia and Covid-19 which have similar symptoms with low computational cost and high confidence. If the embedded model fails to diagnose; a modified ResNet based neural network in the second layer designed to diagnose only Covid-19 with high precision in cloud, the distributed edge to cloud ensemble of neural network models trained and tested on publicly available dataset, has achieved a total accuracy of 93.8% for detection of Covid-19, in compare to 92.4% and 92% accuracy of COVID-Net and inception algorithms respectively. The most challenging part of the work is the accurate diagnosis of Covid-19 and pneumonia diseases from one another with the least amount of error.