Journal Article

A Robust Hybrid Deep Convolutional Neural Network for COVID-19 Disease Identification from Chest X-ray Images

Tabakis, Irene-Maria, Dasygenis, Minas, Sanida, Theodora, Sanida, Maria Vasiliki, Sideris, Argyrios

2023

Overview

The prompt and accurate identification of the causes of pneumonia is necessary to implement rapid treatment and preventative approaches, reduce the burden of infections, and develop more successful intervention strategies. There has been an increase in the number of new pneumonia cases and diseases known as acute respiratory distress syndrome (ARDS) as a direct consequence of the spread of COVID-19. Chest radiography has evolved to the point that it is now an indispensable diagnostic tool for COVID-19 infection pneumonia in hospitals. To fully exploit the technique, it is crucial to design a computer-aided diagnostic (CAD) system to assist doctors and other medical professionals in establishing an accurate and rapid diagnosis of pneumonia. This article presents a robust hybrid deep convolutional neural network (DCNN) for rapidly identifying three categories (normal, COVID-19 and pneumonia (viral or bacterial)) using X-ray image data sourced from the COVID-QU-Ex dataset. The proposed approach on the test set achieved a rate of 99.25% accuracy, 99.10% Kappa-score, 99.43% AUC, 99.24% F1-score, 99.25% recall, and 99.23% precision, respectively. The outcomes of the experiments demonstrate that the presented hybrid DCNN mechanism for identifying three categories utilising X-ray images is robust and effective.

• Publisher: MDPI AG
• Subject: Accuracy / Acute respiratory distress syndrome / Algorithms / Artificial intelligence / Artificial neural networks / Bacterial pneumonia / CAD / chest X-ray images / Chronic obstructive pulmonary disease / Computer aided design / COVID-19 / Datasets / Deep learning / Development and progression / diagnosis / Diagnostic imaging / Health aspects / hybrid DCNN mechanism / Identification / Infection control / Infections / lung opacity / Medical imaging / Medical imaging equipment / Medical personnel / Medical research / Methods / Neural networks / Pandemics / Performance evaluation / Pneumonia / radiography images / Robustness / Viral diseases / X-rays