Deep learning‐based COVID‐19 diagnosis using CT scans with laboratory and physiological parameters

Abstract

The global economy has been dramatically impacted by COVID‐19, which has spread to be a pandemic. COVID‐19 virus affects the respiratory system, causing difficulty breathing in the patient. It is crucial to identify and treat infections as soon as possible. Traditional diagnostic reverse transcription‐polymerase chain reaction (RT‐PCR) methods require more time to find the infection. A high infection rate, slow laboratory analysis, and delayed test results caused the widespread and uncontrolled spread of the disease. This study aims to diagnose the COVID‐19 epidemic by leveraging a modified convolutional neural network (CNN) to quickly and safely predict the disease's appearance from computed tomography (CT) scan images and a laboratory and physiological parameters dataset. A dataset representing 500 patients was used to train, test, and validate the CNN model with results in detecting COVID‐19 having an accuracy, sensitivity, specificity, and F1‐score of 99.33%, 99.09%, 99.52%, and 99.24%, respectively. These experimental results suggest that our strategy performs better than previously published approaches.

Engineered two-dimensional nanomaterials based diagnostics integrated with internet of medical things (IoMT) for COVID-19

“…The suggested model leverages both global and local information to enhance its performance in accurately classifying COVID-19 cases. 303–305 Utilizing AI, the deep neural network employed for pulmonary infection segmentation independently partitions afflicted tissues into distinct components. The authors propose a novel convolutional neural network architecture that utilizes deformation deep embedding to segment anomalies in chest CT images of COVID-19 patients.…”