An Optimized Architecture for COVID‑19 Prediction Using Chest X‑Ray Images

Abstract

In modern times, a disease known as COVID-19 that is highly contagious is continuing to have a profoundly negative influence on the people of the entire world. The fundamental purpose of the model that has been proposed is to improve its predictive capabilities while also providing an effective model for predicting COVID-19 that has a robust diagnostic. Image scaling and noise reduction are two examples of the types of pre-processing techniques that are used at the very first step. The adoption of picture scaling and median filtering techniques, both of which work to enhance the quality of the input data in preparation for further processing steps, allows this goal to be accomplished. Several distinct data augmentation strategies, like flipping and rotation, are utilized to improve the model's performance on a limited dataset and assist it in better comprehending the differences present in the training data. In this article, we will provide a unique Optimized Architecture for COVID-19 Prediction (OACP) model to classify COVID-19 situations as either positive or negative effectively. Using CXR pictures, this novel method, based on a tunable deep learning technique called DenseNet, may predict the presence of COVID-19-positive patients. Based on the findings, it was determined that the proposed model utilized achieved better outcomes, with an accuracy of 98%.