Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images
<p dir="ltr">Chest disease refers to a variety of lung disorders, including lung cancer (LC), COVID-19, pneumonia (PNEU), tuberculosis (TB), and numerous other respiratory disorders. The symptoms (i.e., fever, cough, sore throat, etc.) of these chest diseases are similar, which might...
محفوظ في:
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2023
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| الموضوعات: | |
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| _version_ | 1864513535708495872 |
|---|---|
| author | Hassaan Malik (10486121) |
| author2 | Tayyaba Anees (15373043) Ahmad Sami Al-Shamaylehs (17541423) Salman Z. Alharthi (17541426) Wajeeha Khalil (17541429) Adnan Akhunzada (3134064) |
| author2_role | author author author author author |
| author_facet | Hassaan Malik (10486121) Tayyaba Anees (15373043) Ahmad Sami Al-Shamaylehs (17541423) Salman Z. Alharthi (17541426) Wajeeha Khalil (17541429) Adnan Akhunzada (3134064) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hassaan Malik (10486121) Tayyaba Anees (15373043) Ahmad Sami Al-Shamaylehs (17541423) Salman Z. Alharthi (17541426) Wajeeha Khalil (17541429) Adnan Akhunzada (3134064) |
| dc.date.none.fl_str_mv | 2023-08-26T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/diagnostics13172772 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Deep_Learning-Based_Classification_of_Chest_Diseases_Using_X-rays_CT_Scans_and_Cough_Sound_Images/24717198 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Cardiovascular medicine and haematology Clinical sciences Oncology and carcinogenesis Information and computing sciences Machine learning X-rays deep learning CT scans cough sound COVID-19 lung cancer pneumonia |
| dc.title.none.fl_str_mv | Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Chest disease refers to a variety of lung disorders, including lung cancer (LC), COVID-19, pneumonia (PNEU), tuberculosis (TB), and numerous other respiratory disorders. The symptoms (i.e., fever, cough, sore throat, etc.) of these chest diseases are similar, which might mislead radiologists and health experts when classifying chest diseases. Chest X-rays (CXR), cough sounds, and computed tomography (CT) scans are utilized by researchers and doctors to identify chest diseases such as LC, COVID-19, PNEU, and TB. The objective of the work is to identify nine different types of chest diseases, including COVID-19, edema (EDE), LC, PNEU, pneumothorax (PNEUTH), normal, atelectasis (ATE), and consolidation lung (COL). Therefore, we designed a novel deep learning (DL)-based chest disease detection network (DCDD_Net) that uses a CXR, CT scans, and cough sound images for the identification of nine different types of chest diseases. The scalogram method is used to convert the cough sounds into an image. Before training the proposed DCDD_Net model, the borderline (BL) SMOTE is applied to balance the CXR, CT scans, and cough sound images of nine chest diseases. The proposed DCDD_Net model is trained and evaluated on 20 publicly available benchmark chest disease datasets of CXR, CT scan, and cough sound images. The classification performance of the DCDD_Net is compared with four baseline models, i.e., InceptionResNet-V2, EfficientNet-B0, DenseNet-201, and Xception, as well as state-of-the-art (SOTA) classifiers. The DCDD_Net achieved an accuracy of 96.67%, a precision of 96.82%, a recall of 95.76%, an F1-score of 95.61%, and an area under the curve (AUC) of 99.43%. The results reveal that DCDD_Net outperformed the other four baseline models in terms of many performance evaluation metrics. Thus, the proposed DCDD_Net model can provide significant assistance to radiologists and medical experts. Additionally, the proposed model was also shown to be resilient by statistical evaluations of the datasets using McNemar and ANOVA tests.</p><h2>Other Information</h2><p dir="ltr">Published in: Diagnostics<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/diagnostics13172772" target="_blank">https://dx.doi.org/10.3390/diagnostics13172772</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_234cf3e7ab2532163f48c62ef7f73f87 |
| identifier_str_mv | 10.3390/diagnostics13172772 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24717198 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound ImagesHassaan Malik (10486121)Tayyaba Anees (15373043)Ahmad Sami Al-Shamaylehs (17541423)Salman Z. Alharthi (17541426)Wajeeha Khalil (17541429)Adnan Akhunzada (3134064)Biomedical and clinical sciencesCardiovascular medicine and haematologyClinical sciencesOncology and carcinogenesisInformation and computing sciencesMachine learningX-raysdeep learningCT scanscough soundCOVID-19lung cancerpneumonia<p dir="ltr">Chest disease refers to a variety of lung disorders, including lung cancer (LC), COVID-19, pneumonia (PNEU), tuberculosis (TB), and numerous other respiratory disorders. The symptoms (i.e., fever, cough, sore throat, etc.) of these chest diseases are similar, which might mislead radiologists and health experts when classifying chest diseases. Chest X-rays (CXR), cough sounds, and computed tomography (CT) scans are utilized by researchers and doctors to identify chest diseases such as LC, COVID-19, PNEU, and TB. The objective of the work is to identify nine different types of chest diseases, including COVID-19, edema (EDE), LC, PNEU, pneumothorax (PNEUTH), normal, atelectasis (ATE), and consolidation lung (COL). Therefore, we designed a novel deep learning (DL)-based chest disease detection network (DCDD_Net) that uses a CXR, CT scans, and cough sound images for the identification of nine different types of chest diseases. The scalogram method is used to convert the cough sounds into an image. Before training the proposed DCDD_Net model, the borderline (BL) SMOTE is applied to balance the CXR, CT scans, and cough sound images of nine chest diseases. The proposed DCDD_Net model is trained and evaluated on 20 publicly available benchmark chest disease datasets of CXR, CT scan, and cough sound images. The classification performance of the DCDD_Net is compared with four baseline models, i.e., InceptionResNet-V2, EfficientNet-B0, DenseNet-201, and Xception, as well as state-of-the-art (SOTA) classifiers. The DCDD_Net achieved an accuracy of 96.67%, a precision of 96.82%, a recall of 95.76%, an F1-score of 95.61%, and an area under the curve (AUC) of 99.43%. The results reveal that DCDD_Net outperformed the other four baseline models in terms of many performance evaluation metrics. Thus, the proposed DCDD_Net model can provide significant assistance to radiologists and medical experts. Additionally, the proposed model was also shown to be resilient by statistical evaluations of the datasets using McNemar and ANOVA tests.</p><h2>Other Information</h2><p dir="ltr">Published in: Diagnostics<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/diagnostics13172772" target="_blank">https://dx.doi.org/10.3390/diagnostics13172772</a></p>2023-08-26T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/diagnostics13172772https://figshare.com/articles/journal_contribution/Deep_Learning-Based_Classification_of_Chest_Diseases_Using_X-rays_CT_Scans_and_Cough_Sound_Images/24717198CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247171982023-08-26T03:00:00Z |
| spellingShingle | Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images Hassaan Malik (10486121) Biomedical and clinical sciences Cardiovascular medicine and haematology Clinical sciences Oncology and carcinogenesis Information and computing sciences Machine learning X-rays deep learning CT scans cough sound COVID-19 lung cancer pneumonia |
| status_str | publishedVersion |
| title | Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images |
| title_full | Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images |
| title_fullStr | Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images |
| title_full_unstemmed | Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images |
| title_short | Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images |
| title_sort | Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images |
| topic | Biomedical and clinical sciences Cardiovascular medicine and haematology Clinical sciences Oncology and carcinogenesis Information and computing sciences Machine learning X-rays deep learning CT scans cough sound COVID-19 lung cancer pneumonia |