Medical Diagnosis and Identification of Covid -19 by Intelligent IoT System and Resnet 18 Bilinear Deep Greedy Network

Abstract

An international health crisis has been caused by the widespread COVID-19 epidemic. COVID-19 patient diagnoses are made using deep learning, although this necessitates a massive radiography data collection in order to efficiently deliver an optimum result. This paper presents a novel Intelligent System with IoT sensors for covid 19 and "Bilinear Resnet 18 Deep Greedy Network," which is effective with a limited amount of datasets. Despite peculiarities brought on by a small dataset, the suggested approach could successfully combat the anomalies of over fitting and under fitting. The suggested architecture ensures a successful conclusion when the trained model is correctly evaluated using the provided X-ray datasets of COVID-19 cases. The recommended model offers accuracy of 97%, which is superior to existing methodologies. Better precision, recall, and F1 score are provided; which are 98%, 96%, and 96.94% respectively, which is better than other existing methodology.