Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging
The challenges of respiratory infections persist as a global health crisis, placing substantial stress on healthcare infrastructures and necessitating ongoing investigation into efficacious treatment modalities. The persistent challenge of respiratory infections, including COVID-19, underscores the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/5/500 |
| _version_ | 1827319685249499136 |
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| author | Cheng-Tang Pan Rahul Kumar Zhi-Hong Wen Chih-Hsuan Wang Chun-Yung Chang Yow-Ling Shiue |
| author_facet | Cheng-Tang Pan Rahul Kumar Zhi-Hong Wen Chih-Hsuan Wang Chun-Yung Chang Yow-Ling Shiue |
| author_sort | Cheng-Tang Pan |
| collection | DOAJ |
| description | The challenges of respiratory infections persist as a global health crisis, placing substantial stress on healthcare infrastructures and necessitating ongoing investigation into efficacious treatment modalities. The persistent challenge of respiratory infections, including COVID-19, underscores the critical need for enhanced diagnostic methodologies to support early treatment interventions. This study introduces an innovative two-stage data analytics framework that leverages deep learning algorithms through a strategic combinatorial fusion technique, aimed at refining the accuracy of early-stage diagnosis of such infections. Utilizing a comprehensive dataset compiled from publicly available lung X-ray images, the research employs advanced pre-trained deep learning models to navigate the complexities of disease classification, addressing inherent data imbalances through methodical validation processes. The core contribution of this work lies in its novel application of combinatorial fusion, integrating select models to significantly elevate diagnostic precision. This approach not only showcases the adaptability and strength of deep learning in navigating the intricacies of medical imaging but also marks a significant step forward in the utilization of artificial intelligence to improve outcomes in healthcare diagnostics. The study’s findings illuminate the path toward leveraging technological advancements in enhancing diagnostic accuracies, ultimately contributing to the timely and effective treatment of respiratory diseases. |
| first_indexed | 2024-04-25T00:31:48Z |
| format | Article |
| id | doaj.art-00f45db714b1478585bb5d42302656de |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-04-25T00:31:48Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-00f45db714b1478585bb5d42302656de2024-03-12T16:41:58ZengMDPI AGDiagnostics2075-44182024-02-0114550010.3390/diagnostics14050500Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray ImagingCheng-Tang Pan0Rahul Kumar1Zhi-Hong Wen2Chih-Hsuan Wang3Chun-Yung Chang4Yow-Ling Shiue5Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, TaiwanDepartment of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, TaiwanDepartment of Marine Biotechnology and Research, National Sun Yat-sen University, Kaohsiung 804, TaiwanDivision of Nephrology and Metabolism, Department of Internal Medicine, Kaohsiung Armed Forces General Hospital, Kaohsiung 804, TaiwanDivision of Nephrology and Metabolism, Department of Internal Medicine, Kaohsiung Armed Forces General Hospital, Kaohsiung 804, TaiwanInstitute of Precision Medicine, National Sun Yat-sen University, Kaohsiung 804, TaiwanThe challenges of respiratory infections persist as a global health crisis, placing substantial stress on healthcare infrastructures and necessitating ongoing investigation into efficacious treatment modalities. The persistent challenge of respiratory infections, including COVID-19, underscores the critical need for enhanced diagnostic methodologies to support early treatment interventions. This study introduces an innovative two-stage data analytics framework that leverages deep learning algorithms through a strategic combinatorial fusion technique, aimed at refining the accuracy of early-stage diagnosis of such infections. Utilizing a comprehensive dataset compiled from publicly available lung X-ray images, the research employs advanced pre-trained deep learning models to navigate the complexities of disease classification, addressing inherent data imbalances through methodical validation processes. The core contribution of this work lies in its novel application of combinatorial fusion, integrating select models to significantly elevate diagnostic precision. This approach not only showcases the adaptability and strength of deep learning in navigating the intricacies of medical imaging but also marks a significant step forward in the utilization of artificial intelligence to improve outcomes in healthcare diagnostics. The study’s findings illuminate the path toward leveraging technological advancements in enhancing diagnostic accuracies, ultimately contributing to the timely and effective treatment of respiratory diseases.https://www.mdpi.com/2075-4418/14/5/500respiratory infectionsdeep learningconvolutional neural network (CNN)lung X-ray imagescombinatorial fusion |
| spellingShingle | Cheng-Tang Pan Rahul Kumar Zhi-Hong Wen Chih-Hsuan Wang Chun-Yung Chang Yow-Ling Shiue Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging Diagnostics respiratory infections deep learning convolutional neural network (CNN) lung X-ray images combinatorial fusion |
| title | Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging |
| title_full | Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging |
| title_fullStr | Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging |
| title_full_unstemmed | Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging |
| title_short | Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging |
| title_sort | improving respiratory infection diagnosis with deep learning and combinatorial fusion a two stage approach using chest x ray imaging |
| topic | respiratory infections deep learning convolutional neural network (CNN) lung X-ray images combinatorial fusion |
| url | https://www.mdpi.com/2075-4418/14/5/500 |
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