A Deep Learning Framework for Prognosis Patients with COVID-19

A branch of AI known as "deep learning" sifts through mountains of data in search of patterns and predictions using a machine learning approach called an artificial neural network. Rapid progress and widespread, successful application across many fields have been its hallmarks in recent years. A significant obstacle to healthcare transformation is the learning of useful information from complicated, high-dimensional, and diverse biological data. This research aims to give a concise synopsis of recent developments in deep learning, a survey of deep learning studies conducted on COVID-19, and some realworld applications of deep-learning(DL) strategies' for COVID19 diagnosis, prognosis, and therapy management. DL has the potential to enhance the quality and speed of drug development by analyzing medicinal imaging data, lab test results, and other pertinent data to make diagnoses, assess the course and prognosis of illnesses, and even provide healing recommendations and drug-utilize regimens. In addition, it may guide policymakers in creating effective control and preventative strategies. Additionally, we evaluate the present state of deep learning's capabilities and shortcomings in terms of COVID-19 treatment accuracy, covering issues such as the dearth of phenotypically plentiful data and the necessity for deeper learning models that are easier to understand and work with. We wrap up by talking about how to get beyond the obstacles that are standing in the way of deep learning's potential future clinical uses.