C19-MLE: A Multi-Layer Ensemble Deep Learning Approach for COVID-19 Detection Using Cough Sounds and X-Ray Imaging

作     者：Hussain, Shabir Amran, Gehad Abdullah Alabrah, Amerah Alkhalil, Lubna Al-Bakhrani, Ali A. 

作者机构：Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Inst Biopharmaceut & Hlth Engn Shenzhen 518055 Peoples R China Dalian Univ Technol Dept Management Sci & Engn Dalian 116024 Peoples R China King Saud Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh 11543 Saudi Arabia Dalian Univ Technol Coll Software Dalian 116042 Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2024年第12卷

页      面：197151-197167页

核心收录：

基　　金：Researchers Supporting Project number  King Saud University  Riyadh  Saudi Arabia [RSP2024R476] 

主　　题：COVID-19 X-ray imaging Biomedical imaging Deep learning Accuracy Feature extraction Convolutional neural networks Pipelines Pandemics Mel frequency cepstral coefficient COVID-19 detection cough audio analysis radiographic imaging ensemble methods deep learning autoencoder segmentation 

摘      要：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.