A CNN based coronavirus disease prediction system for chest X-rays

Umair Hafeez; Muhammad Umer; Ahmad Hameed; Hassan Mustafa; Ahmed Sohaib; Michele Nappi; Hamza Madni

doi:10.1007/s12652-022-03775-3

A CNN based coronavirus disease prediction system for chest X-rays

Abstrakt

Coronavirus disease (COVID-19) proliferated globally in early 2020, causing existential dread in the whole world. Radiography is crucial in the clinical staging and diagnosis of COVID-19 and offers high potential to improve healthcare plans for tackling the pandemic. However high variations in infection characteristics and low contrast between normal and infected regions pose great challenges in preparing radiological reports. To address these challenges, this study presents CODISCCNN (CNN based Coronavirus DIsease Prediction System for Chest X-rays) that can automatically extract the features from chest X-ray images for the disease prediction. However, to get the infected region of X-ray, edges of the images are detected by applying image preprocessing. Furthermore, to attenuate the shortage of labeled datasets data augmentation has been adapted. Extensive experiments have been performed to classify X-ray images into two classes (Normal and COVID), three classes (Normal, COVID, and Virus Bacteria), and four classes (Normal, COVID, and Virus Bacteria, and Virus Pneumonia) with the accuracy of 97%, 89%, and 84% respectively. The proposed CNN-based model outperforms many cutting-edge classifcation models and boosts state-of-the-art performance.

Cytowania

2 0

CrossRef
0

Web of Science
1 6

Scopus

Autorzy (7)

Umair Hafeez
Muhammad Umer
Ahmad Hameed
Hassan Mustafa
Ahmed Sohaib
Michele Nappi
Hamza Madni dr

Cytuj jako

Pełna treść

pełna treść publikacji nie jest dostępna w portalu

pełna treść artykułu zobacz w serwisie zewnętrznym otwiera się w nowej karcie

Słowa kluczowe

COVID-19, X-RAY IMAGES, DEEP LEARNING, CONVOLUTIONAL NEURAL NETWORK

Informacje szczegółowe

Kategoria:

Publikacja w czasopiśmie

Typ:

artykuły w czasopismach

Opublikowano w:

Journal of Ambient Intelligence and Humanized Computing nr 14, strony 13179 - 13193,
ISSN: 1868-5137

Język:

angielski

Rok wydania:

2023

Opis bibliograficzny:

Hafeez U., Umer M., Hameed A., Mustafa H., Sohaib A., Nappi M., Madni H.: A CNN based coronavirus disease prediction system for chest X-rays// Journal of Ambient Intelligence and Humanized Computing -Vol. 14, (2023), s.13179-13193

DOI: