A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images
In 2019, the world experienced the rapid outbreak of the Covid-19 pandemic creating an alarming situation worldwide. The virus targets the respiratory system causing pneumonia with other symptoms such as fatigue, dry cough, and fever which can be mistakenly diagnosed as pneumonia, lung cancer, or TB...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016822007104 |
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| author | Goram Mufarah M. Alshmrani Qiang Ni Richard Jiang Haris Pervaiz Nada M. Elshennawy |
| author_facet | Goram Mufarah M. Alshmrani Qiang Ni Richard Jiang Haris Pervaiz Nada M. Elshennawy |
| author_sort | Goram Mufarah M. Alshmrani |
| collection | DOAJ |
| description | In 2019, the world experienced the rapid outbreak of the Covid-19 pandemic creating an alarming situation worldwide. The virus targets the respiratory system causing pneumonia with other symptoms such as fatigue, dry cough, and fever which can be mistakenly diagnosed as pneumonia, lung cancer, or TB. Thus, the early diagnosis of COVID-19 is critical since the disease can provoke patients’ mortality. Chest X-ray (CXR) is commonly employed in healthcare sector where both quick and precise diagnosis can be supplied. Deep learning algorithms have proved extraordinary capabilities in terms of lung diseases detection and classification. They facilitate and expedite the diagnosis process and save time for the medical practitioners. In this paper, a deep learning (DL) architecture for multi-class classification of Pneumonia, Lung Cancer, tuberculosis (TB), Lung Opacity, and most recently COVID-19 is proposed. Tremendous CXR images of 3615 COVID-19, 6012 Lung opacity, 5870 Pneumonia, 20,000 lung cancer, 1400 tuberculosis, and 10,192 normal images were resized, normalized, and randomly split to fit the DL requirements. In terms of classification, we utilized a pre-trained model, VGG19 followed by three blocks of convolutional neural network (CNN) as a feature extraction and fully connected network at the classification stage. The experimental results revealed that our proposed VGG19 + CNN outperformed other existing work with 96.48 % accuracy, 93.75 % recall, 97.56 % precision, 95.62 % F1 score, and 99.82 % area under the curve (AUC). The proposed model delivered superior performance allowing healthcare practitioners to diagnose and treat patients more quickly and efficiently. |
| first_indexed | 2024-04-11T00:54:21Z |
| format | Article |
| id | doaj.art-5c0a3064686a4dbdb21369ea9ac062cc |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-04-11T00:54:21Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-5c0a3064686a4dbdb21369ea9ac062cc2023-01-05T06:46:36ZengElsevierAlexandria Engineering Journal1110-01682023-02-0164923935A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) imagesGoram Mufarah M. Alshmrani0Qiang Ni1Richard Jiang2Haris Pervaiz3Nada M. Elshennawy4School of Computing and Commutations, Lancaster University, Lancaster LA1 4YW, UK; Corresponding author.School of Computing and Commutations, Lancaster University, Lancaster LA1 4YW, UKSchool of Computing and Commutations, Lancaster University, Lancaster LA1 4YW, UKSchool of Computing and Commutations, Lancaster University, Lancaster LA1 4YW, UKDepartment of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, EgyptIn 2019, the world experienced the rapid outbreak of the Covid-19 pandemic creating an alarming situation worldwide. The virus targets the respiratory system causing pneumonia with other symptoms such as fatigue, dry cough, and fever which can be mistakenly diagnosed as pneumonia, lung cancer, or TB. Thus, the early diagnosis of COVID-19 is critical since the disease can provoke patients’ mortality. Chest X-ray (CXR) is commonly employed in healthcare sector where both quick and precise diagnosis can be supplied. Deep learning algorithms have proved extraordinary capabilities in terms of lung diseases detection and classification. They facilitate and expedite the diagnosis process and save time for the medical practitioners. In this paper, a deep learning (DL) architecture for multi-class classification of Pneumonia, Lung Cancer, tuberculosis (TB), Lung Opacity, and most recently COVID-19 is proposed. Tremendous CXR images of 3615 COVID-19, 6012 Lung opacity, 5870 Pneumonia, 20,000 lung cancer, 1400 tuberculosis, and 10,192 normal images were resized, normalized, and randomly split to fit the DL requirements. In terms of classification, we utilized a pre-trained model, VGG19 followed by three blocks of convolutional neural network (CNN) as a feature extraction and fully connected network at the classification stage. The experimental results revealed that our proposed VGG19 + CNN outperformed other existing work with 96.48 % accuracy, 93.75 % recall, 97.56 % precision, 95.62 % F1 score, and 99.82 % area under the curve (AUC). The proposed model delivered superior performance allowing healthcare practitioners to diagnose and treat patients more quickly and efficiently.http://www.sciencedirect.com/science/article/pii/S1110016822007104PneumoniaLung cancerCOVID-19, TB, Lung opacityX-ray imagesDeep learning, VGG19 +CNNMulticlass diseases classification |
| spellingShingle | Goram Mufarah M. Alshmrani Qiang Ni Richard Jiang Haris Pervaiz Nada M. Elshennawy A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images Alexandria Engineering Journal Pneumonia Lung cancer COVID-19, TB, Lung opacity X-ray images Deep learning, VGG19 +CNN Multiclass diseases classification |
| title | A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images |
| title_full | A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images |
| title_fullStr | A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images |
| title_full_unstemmed | A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images |
| title_short | A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images |
| title_sort | deep learning architecture for multi class lung diseases classification using chest x ray cxr images |
| topic | Pneumonia Lung cancer COVID-19, TB, Lung opacity X-ray images Deep learning, VGG19 +CNN Multiclass diseases classification |
| url | http://www.sciencedirect.com/science/article/pii/S1110016822007104 |
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