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Mahmoud Aly

Bio: Mahmoud Aly is an academic researcher from Minia University. The author has contributed to research in topics: Human voice & Respiratory sounds. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors used the Coswara dataset where each user has recorded 9 different types of sounds as cough, breathing, and speech labeled with COVID-19 status and showed that using simple binary classifiers can achieve an AUC of 96.4% and an accuracy of 96% by averaging the predictions of multiple models trained separately on different sound types.
Abstract: Since the outbreak of COVID-19, many efforts have been made to utilize the respiratory sounds and coughs collected by smartphones for training Machine Learning models to classify and distinguish COVID-19 sounds from healthy ones. Embedding those models into mobile applications or Internet of things devices can make effective COVID-19 pre-screening tools afforded by anyone anywhere. Most of the previous researchers trained their classifiers with respiratory sounds such as breathing or coughs, and they achieved promising results. We claim that using special voice patterns besides other respiratory sounds can achieve better performance. In this study, we used the Coswara dataset where each user has recorded 9 different types of sounds as cough, breathing, and speech labeled with COVID-19 status. A combination of models trained on different sounds can diagnose COVID-19 more accurately than a single model trained on cough or breathing only. Our results show that using simple binary classifiers can achieve an AUC of 96.4% and an accuracy of 96% by averaging the predictions of multiple models trained and evaluated separately on different sound types. Finally, this study aims to draw attention to the importance of the human voice alongside other respiratory sounds for the sound-based COVID-19 diagnosis.

23 citations


Cited by
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Journal ArticleDOI
TL;DR: A comprehensive review of the applications of Artificial Intelligence in the detection of coronavirus using modalities such as CT-Scans, X-rays, Cough sounds, MRIs, ultrasound and clinical markers are explored in depth as discussed by the authors .
Abstract: Abstract In early March 2020, the World Health Organization (WHO) proclaimed the novel COVID-19 as a global pandemic. The coronavirus went on to be a life-threatening infection and is still wreaking havoc all around the globe. Though vaccines have been rolled out, a section of the population (the elderly and people with comorbidities) still succumb to this deadly illness. Hence, it is imperative to diagnose this infection early to prevent a potential severe prognosis. This contagious disease is usually diagnosed using a conventional technique called the Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, this procedure leads to a number of wrong and false-negative results. Moreover, it might also not diagnose the newer variants of this mutating virus. Artificial Intelligence has been one of the most widely discussed topics in recent years. It is widely used to tackle various issues across multiple domains in the modern world. In this extensive review, the applications of Artificial Intelligence in the detection of coronavirus using modalities such as CT-Scans, X-rays, Cough sounds, MRIs, ultrasound and clinical markers are explored in depth. This review also provides data enthusiasts and the broader health community with a complete assessment of the current state-of-the-art approaches in diagnosing COVID-19. The key issues and future directions are also provided for upcoming researchers.

17 citations

Journal ArticleDOI
TL;DR: A comprehensive review of the applications of Artificial Intelligence in the detection of coronavirus using modalities such as CT-Scans, X-rays, Cough sounds, MRIs, ultrasound and clinical markers are explored in depth as discussed by the authors .
Abstract: Abstract In early March 2020, the World Health Organization (WHO) proclaimed the novel COVID-19 as a global pandemic. The coronavirus went on to be a life-threatening infection and is still wreaking havoc all around the globe. Though vaccines have been rolled out, a section of the population (the elderly and people with comorbidities) still succumb to this deadly illness. Hence, it is imperative to diagnose this infection early to prevent a potential severe prognosis. This contagious disease is usually diagnosed using a conventional technique called the Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, this procedure leads to a number of wrong and false-negative results. Moreover, it might also not diagnose the newer variants of this mutating virus. Artificial Intelligence has been one of the most widely discussed topics in recent years. It is widely used to tackle various issues across multiple domains in the modern world. In this extensive review, the applications of Artificial Intelligence in the detection of coronavirus using modalities such as CT-Scans, X-rays, Cough sounds, MRIs, ultrasound and clinical markers are explored in depth. This review also provides data enthusiasts and the broader health community with a complete assessment of the current state-of-the-art approaches in diagnosing COVID-19. The key issues and future directions are also provided for upcoming researchers.

15 citations

Journal ArticleDOI
TL;DR: In this article , a novel approach is proposed to pre-process the data and pass it through a newly proposed CNN architecture, which helps to make an accurate diagnosis of lung sounds.
Abstract: Every respiratory-related checkup includes audio samples collected from the individual, collected through different tools (sonograph, stethoscope). This audio is analyzed to identify pathology, which requires time and effort. The research work proposed in this paper aims at easing the task with deep learning by the diagnosis of lung-related pathologies using Convolutional Neural Network (CNN) with the help of transformed features from the audio samples. International Conference on Biomedical and Health Informatics (ICBHI) corpus dataset was used for lung sound. Here a novel approach is proposed to pre-process the data and pass it through a newly proposed CNN architecture. The combination of pre-processing steps MFCC, Melspectrogram, and Chroma CENS with CNN improvise the performance of the proposed system, which helps to make an accurate diagnosis of lung sounds. The comparative analysis shows how the proposed approach performs better with previous state-of-the-art research approaches. It also shows that there is no need for a wheeze or a crackle to be present in the lung sound to carry out the classification of respiratory pathologies.

7 citations

Journal ArticleDOI
TL;DR: In this article , an interval temporal logic decision tree extraction algorithm was proposed to improve the performance of symbolic learning for automated classification of COVID-19-positive cough and breath recordings.

4 citations

Journal ArticleDOI
TL;DR: In this article , three different signal analyses have been applied (per broadband, per sub-bands and per broadband & subbands) to Cough, Breathing and Speech signals of Coswara dataset to extract nonlinear patterns (Energy, Entropies, Correlation Dimension, Detrended Fluctuation Analysis, Lyapunov Exponent & Fractal Dimensions) for feeding a XGBoost classifier to discriminate COVID-19 activity on its different stages.

2 citations