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Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images
Medical imaging is considered a suitable alternative testing method for the detection of lung diseases. Many researchers have been working to develop various detection methods that have aided in the prevention of lung diseases. To better understand the condition of the lung disease infection, chest...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632494/ https://www.ncbi.nlm.nih.gov/pubmed/37938631 http://dx.doi.org/10.1038/s41598-023-46147-3 |
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author | Al-Sheikh, Mona Hmoud Al Dandan, Omran Al-Shamayleh, Ahmad Sami Jalab, Hamid A. Ibrahim, Rabha W. |
author_facet | Al-Sheikh, Mona Hmoud Al Dandan, Omran Al-Shamayleh, Ahmad Sami Jalab, Hamid A. Ibrahim, Rabha W. |
author_sort | Al-Sheikh, Mona Hmoud |
collection | PubMed |
description | Medical imaging is considered a suitable alternative testing method for the detection of lung diseases. Many researchers have been working to develop various detection methods that have aided in the prevention of lung diseases. To better understand the condition of the lung disease infection, chest X-Ray and CT scans are utilized to check the disease’s spread throughout the lungs. This study proposes an automated system for the detection multi lung diseases in X-Ray and CT scans. A customized convolutional neural network (CNN) and two pre-trained deep learning models with a new image enhancement model are proposed for image classification. The proposed lung disease detection comprises two main steps: pre-processing, and deep learning classification. The new image enhancement algorithm is developed in the pre-processing step using k-symbol Lerch transcendent functions model which enhancement images based on image pixel probability. While, in the classification step, the customized CNN architecture and two pre-trained CNN models Alex Net, and VGG16Net are developed. The proposed approach was tested on publicly available image datasets (CT, and X-Ray image dataset), and the results showed classification accuracy, sensitivity, and specificity of 98.60%, 98.40%, and 98.50% for the X-Ray image dataset, respectively, and 98.80%, 98.50%, 98.40% for the CT scans dataset, respectively. Overall, the obtained results highlight the advantages of the image enhancement model as a first step in processing. |
format | Online Article Text |
id | pubmed-10632494 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106324942023-11-10 Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images Al-Sheikh, Mona Hmoud Al Dandan, Omran Al-Shamayleh, Ahmad Sami Jalab, Hamid A. Ibrahim, Rabha W. Sci Rep Article Medical imaging is considered a suitable alternative testing method for the detection of lung diseases. Many researchers have been working to develop various detection methods that have aided in the prevention of lung diseases. To better understand the condition of the lung disease infection, chest X-Ray and CT scans are utilized to check the disease’s spread throughout the lungs. This study proposes an automated system for the detection multi lung diseases in X-Ray and CT scans. A customized convolutional neural network (CNN) and two pre-trained deep learning models with a new image enhancement model are proposed for image classification. The proposed lung disease detection comprises two main steps: pre-processing, and deep learning classification. The new image enhancement algorithm is developed in the pre-processing step using k-symbol Lerch transcendent functions model which enhancement images based on image pixel probability. While, in the classification step, the customized CNN architecture and two pre-trained CNN models Alex Net, and VGG16Net are developed. The proposed approach was tested on publicly available image datasets (CT, and X-Ray image dataset), and the results showed classification accuracy, sensitivity, and specificity of 98.60%, 98.40%, and 98.50% for the X-Ray image dataset, respectively, and 98.80%, 98.50%, 98.40% for the CT scans dataset, respectively. Overall, the obtained results highlight the advantages of the image enhancement model as a first step in processing. Nature Publishing Group UK 2023-11-08 /pmc/articles/PMC10632494/ /pubmed/37938631 http://dx.doi.org/10.1038/s41598-023-46147-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Al-Sheikh, Mona Hmoud Al Dandan, Omran Al-Shamayleh, Ahmad Sami Jalab, Hamid A. Ibrahim, Rabha W. Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images |
title | Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images |
title_full | Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images |
title_fullStr | Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images |
title_full_unstemmed | Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images |
title_short | Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images |
title_sort | multi-class deep learning architecture for classifying lung diseases from chest x-ray and ct images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632494/ https://www.ncbi.nlm.nih.gov/pubmed/37938631 http://dx.doi.org/10.1038/s41598-023-46147-3 |
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