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Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning
COVID-19 has infected millions of people worldwide over the past few years. The main technique used for COVID-19 detection is reverse transcription, which is expensive, sensitive, and requires medical expertise. X-ray imaging is an alternative and more accessible technique. This study aimed to impro...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9610066/ https://www.ncbi.nlm.nih.gov/pubmed/36298328 http://dx.doi.org/10.3390/s22207977 |
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author | Naseem, Muhammad Tahir Hussain, Tajmal Lee, Chan-Su Khan, Muhammad Adnan |
author_facet | Naseem, Muhammad Tahir Hussain, Tajmal Lee, Chan-Su Khan, Muhammad Adnan |
author_sort | Naseem, Muhammad Tahir |
collection | PubMed |
description | COVID-19 has infected millions of people worldwide over the past few years. The main technique used for COVID-19 detection is reverse transcription, which is expensive, sensitive, and requires medical expertise. X-ray imaging is an alternative and more accessible technique. This study aimed to improve detection accuracy to create a computer-aided diagnostic tool. Combining other artificial intelligence applications techniques with radiological imaging can help detect different diseases. This study proposes a technique for the automatic detection of COVID-19 and other chest-related diseases using digital chest X-ray images of suspected patients by applying transfer learning (TL) algorithms. For this purpose, two balanced datasets, Dataset-1 and Dataset-2, were created by combining four public databases and collecting images from recently published articles. Dataset-1 consisted of 6000 chest X-ray images with 1500 for each class. Dataset-2 consisted of 7200 images with 1200 for each class. To train and test the model, TL with nine pretrained convolutional neural networks (CNNs) was used with augmentation as a preprocessing method. The network was trained to classify using five classifiers: two-class classifier (normal and COVID-19); three-class classifier (normal, COVID-19, and viral pneumonia), four-class classifier (normal, viral pneumonia, COVID-19, and tuberculosis (Tb)), five-class classifier (normal, bacterial pneumonia, COVID-19, Tb, and pneumothorax), and six-class classifier (normal, bacterial pneumonia, COVID-19, viral pneumonia, Tb, and pneumothorax). For two, three, four, five, and six classes, our model achieved a maximum accuracy of 99.83, 98.11, 97.00, 94.66, and 87.29%, respectively. |
format | Online Article Text |
id | pubmed-9610066 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96100662022-10-28 Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning Naseem, Muhammad Tahir Hussain, Tajmal Lee, Chan-Su Khan, Muhammad Adnan Sensors (Basel) Article COVID-19 has infected millions of people worldwide over the past few years. The main technique used for COVID-19 detection is reverse transcription, which is expensive, sensitive, and requires medical expertise. X-ray imaging is an alternative and more accessible technique. This study aimed to improve detection accuracy to create a computer-aided diagnostic tool. Combining other artificial intelligence applications techniques with radiological imaging can help detect different diseases. This study proposes a technique for the automatic detection of COVID-19 and other chest-related diseases using digital chest X-ray images of suspected patients by applying transfer learning (TL) algorithms. For this purpose, two balanced datasets, Dataset-1 and Dataset-2, were created by combining four public databases and collecting images from recently published articles. Dataset-1 consisted of 6000 chest X-ray images with 1500 for each class. Dataset-2 consisted of 7200 images with 1200 for each class. To train and test the model, TL with nine pretrained convolutional neural networks (CNNs) was used with augmentation as a preprocessing method. The network was trained to classify using five classifiers: two-class classifier (normal and COVID-19); three-class classifier (normal, COVID-19, and viral pneumonia), four-class classifier (normal, viral pneumonia, COVID-19, and tuberculosis (Tb)), five-class classifier (normal, bacterial pneumonia, COVID-19, Tb, and pneumothorax), and six-class classifier (normal, bacterial pneumonia, COVID-19, viral pneumonia, Tb, and pneumothorax). For two, three, four, five, and six classes, our model achieved a maximum accuracy of 99.83, 98.11, 97.00, 94.66, and 87.29%, respectively. MDPI 2022-10-19 /pmc/articles/PMC9610066/ /pubmed/36298328 http://dx.doi.org/10.3390/s22207977 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Naseem, Muhammad Tahir Hussain, Tajmal Lee, Chan-Su Khan, Muhammad Adnan Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning |
title | Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning |
title_full | Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning |
title_fullStr | Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning |
title_full_unstemmed | Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning |
title_short | Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning |
title_sort | classification and detection of covid-19 and other chest-related diseases using transfer learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9610066/ https://www.ncbi.nlm.nih.gov/pubmed/36298328 http://dx.doi.org/10.3390/s22207977 |
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