C19-MLE: A Multi-Layer Ensemble Deep Learning Approach for COVID-19 Detection Using Cough Sounds and X-Ray Imaging
The COVID-19 pandemic highlighted the urgent need for rapid and efficient screening methods, leading to a growing demand for alternatives to resource-intensive RT-PCR tests. Among these, intelligent, contact-free automated systems emerged as a promising solution for quick preliminary COVID-19 detect...
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2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10813173/ |
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| author | Shabir Hussain Gehad Abdullah Amran Amerah Alabrah Lubna Alkhalil Ali A. Al-Bakhrani |
| author_facet | Shabir Hussain Gehad Abdullah Amran Amerah Alabrah Lubna Alkhalil Ali A. Al-Bakhrani |
| author_sort | Shabir Hussain |
| collection | DOAJ |
| description | The COVID-19 pandemic highlighted the urgent need for rapid and efficient screening methods, leading to a growing demand for alternatives to resource-intensive RT-PCR tests. Among these, intelligent, contact-free automated systems emerged as a promising solution for quick preliminary COVID-19 detection. This study introduces the COVID-19 Multi-Layer Ensemble framework (C19-MLE), designed to enhance the accuracy of COVID-19 detection. The approach begins with a 2D convolutional neural network (CNN) combined with a variation autoencoder for precise classification of cough sounds. Additionally, a UNet-based encoder-decoder architecture is used for segmenting chest X-ray images. These segmented images are then classified using two models, ResNet-50 and Inception V3, and their results are combined using an ensemble learning technique. This first-layer ensemble achieves an impressive accuracy of 98.5% in classifying chest X-rays. Meanwhile, the proposed 2D CNN model for cough classification achieves an accuracy of 97.79%. The second-layer ensemble, which fuses the results of both chest X-ray and cough classifications using a meta-classifier with a hard prediction and weighted sum-rule technique, achieves a remarkable overall accuracy of 99.89%. The C19-MLE framework demonstrates the powerful synergy between cough audio signals and chest X-ray images, providing a highly accurate method for preliminary and post-screening COVID-19 diagnosis. The high accuracy of this model highlights its potential as a crucial tool for early disease detection and prevention, especially in settings where resources are limited. |
| format | Article |
| id | doaj-art-9fc4f40ff650415ebad90ce93ad1648d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9fc4f40ff650415ebad90ce93ad1648d2024-12-31T00:00:49ZengIEEEIEEE Access2169-35362024-01-011219715119716710.1109/ACCESS.2024.352193810813173C19-MLE: A Multi-Layer Ensemble Deep Learning Approach for COVID-19 Detection Using Cough Sounds and X-Ray ImagingShabir Hussain0Gehad Abdullah Amran1https://orcid.org/0000-0002-4304-4199Amerah Alabrah2https://orcid.org/0000-0001-9750-3883Lubna Alkhalil3Ali A. Al-Bakhrani4https://orcid.org/0000-0003-1360-8640Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, ChinaDepartment of Management Science and Engineering, Dalian University of Technology, Dalian, ChinaDepartment of Information Systems, College of Computer and Information Science, King Saud University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Science, King Saud University, Riyadh, Saudi ArabiaCollege of Software Engineering, Dalian University of Technology, Dalian, ChinaThe COVID-19 pandemic highlighted the urgent need for rapid and efficient screening methods, leading to a growing demand for alternatives to resource-intensive RT-PCR tests. Among these, intelligent, contact-free automated systems emerged as a promising solution for quick preliminary COVID-19 detection. This study introduces the COVID-19 Multi-Layer Ensemble framework (C19-MLE), designed to enhance the accuracy of COVID-19 detection. The approach begins with a 2D convolutional neural network (CNN) combined with a variation autoencoder for precise classification of cough sounds. Additionally, a UNet-based encoder-decoder architecture is used for segmenting chest X-ray images. These segmented images are then classified using two models, ResNet-50 and Inception V3, and their results are combined using an ensemble learning technique. This first-layer ensemble achieves an impressive accuracy of 98.5% in classifying chest X-rays. Meanwhile, the proposed 2D CNN model for cough classification achieves an accuracy of 97.79%. The second-layer ensemble, which fuses the results of both chest X-ray and cough classifications using a meta-classifier with a hard prediction and weighted sum-rule technique, achieves a remarkable overall accuracy of 99.89%. The C19-MLE framework demonstrates the powerful synergy between cough audio signals and chest X-ray images, providing a highly accurate method for preliminary and post-screening COVID-19 diagnosis. The high accuracy of this model highlights its potential as a crucial tool for early disease detection and prevention, especially in settings where resources are limited.https://ieeexplore.ieee.org/document/10813173/COVID-19 detectioncough audio analysisradiographic imagingensemble methodsdeep learning autoencodersegmentation |
| spellingShingle | Shabir Hussain Gehad Abdullah Amran Amerah Alabrah Lubna Alkhalil Ali A. Al-Bakhrani C19-MLE: A Multi-Layer Ensemble Deep Learning Approach for COVID-19 Detection Using Cough Sounds and X-Ray Imaging IEEE Access COVID-19 detection cough audio analysis radiographic imaging ensemble methods deep learning autoencoder segmentation |
| title | C19-MLE: A Multi-Layer Ensemble Deep Learning Approach for COVID-19 Detection Using Cough Sounds and X-Ray Imaging |
| title_full | C19-MLE: A Multi-Layer Ensemble Deep Learning Approach for COVID-19 Detection Using Cough Sounds and X-Ray Imaging |
| title_fullStr | C19-MLE: A Multi-Layer Ensemble Deep Learning Approach for COVID-19 Detection Using Cough Sounds and X-Ray Imaging |
| title_full_unstemmed | C19-MLE: A Multi-Layer Ensemble Deep Learning Approach for COVID-19 Detection Using Cough Sounds and X-Ray Imaging |
| title_short | C19-MLE: A Multi-Layer Ensemble Deep Learning Approach for COVID-19 Detection Using Cough Sounds and X-Ray Imaging |
| title_sort | c19 mle a multi layer ensemble deep learning approach for covid 19 detection using cough sounds and x ray imaging |
| topic | COVID-19 detection cough audio analysis radiographic imaging ensemble methods deep learning autoencoder segmentation |
| url | https://ieeexplore.ieee.org/document/10813173/ |
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