An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works

Gürsoy, Ercan; Kaya, Yasin

doi:10.1007/s00530-023-01083-0

Cited by 17 publications

(8 citation statements)

References 144 publications

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“…In-depth tests with CXR were carried out, and the results showed that the proposed model not only achieves an accuracy of 92.45% but also manages to successfully maintain the confidentiality of the data for a wide range of clients. Topff et al [ 72 ] developed a novel CNN [ 73 ] model for the classification of COVID-19, and they achieved remarkable outcomes in terms of a sensitivity of 0.87 and a specificity of 0.94. Lande et al [ 74 ] designed a DL model for the Omicron [ 75 ] variant of COVID-19 topic modeling.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

Malik,

Anees

2024

PLoS ONE

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Chest disease refers to a wide range of conditions affecting the lungs, such as COVID-19, lung cancer (LC), consolidation lung (COL), and many more. When diagnosing chest disorders medical professionals may be thrown off by the overlapping symptoms (such as fever, cough, sore throat, etc.). Additionally, researchers and medical professionals make use of chest X-rays (CXR), cough sounds, and computed tomography (CT) scans to diagnose chest disorders. The present study aims to classify the nine different conditions of chest disorders, including COVID-19, LC, COL, atelectasis (ATE), tuberculosis (TB), pneumothorax (PNEUTH), edema (EDE), pneumonia (PNEU). Thus, we suggested four novel convolutional neural network (CNN) models that train distinct image-level representations for nine different chest disease classifications by extracting features from images. Furthermore, the proposed CNN employed several new approaches such as a max-pooling layer, batch normalization layers (BANL), dropout, rank-based average pooling (RBAP), and multiple-way data generation (MWDG). The scalogram method is utilized to transform the sounds of coughing into a visual representation. Before beginning to train the model that has been developed, the SMOTE approach is used to calibrate the CXR and CT scans as well as the cough sound images (CSI) of nine different chest disorders. The CXR, CT scan, and CSI used for training and evaluating the proposed model come from 24 publicly available benchmark chest illness datasets. The classification performance of the proposed model is compared with that of seven baseline models, namely Vgg-19, ResNet-101, ResNet-50, DenseNet-121, EfficientNetB0, DenseNet-201, and Inception-V3, in addition to state-of-the-art (SOTA) classifiers. The effectiveness of the proposed model is further demonstrated by the results of the ablation experiments. The proposed model was successful in achieving an accuracy of 99.01%, making it superior to both the baseline models and the SOTA classifiers. As a result, the proposed approach is capable of offering significant support to radiologists and other medical professionals.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

Malik,

Anees

2024

PLoS ONE

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“…A detailed overview was done by the authors in [ 22 ] that discusses the different data types in COVID-19 detection using deep learning and machine learning. They have included the used data processing and the methodology for each study.…”

Section: Related Workmentioning

confidence: 99%

A tree-based explainable AI model for early detection of Covid-19 using physiological data

Talib,

Afadar,

Nasir

et al. 2024

BMC Med Inform Decis Mak

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With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing Artificial Intelligence (AI) and Data Science techniques for disease detection. Although COVID-19 cases have declined, there are still cases and deaths around the world. Therefore, early detection of COVID-19 before the onset of symptoms has become crucial in reducing its extensive impact. Fortunately, wearable devices such as smartwatches have proven to be valuable sources of physiological data, including Heart Rate (HR) and sleep quality, enabling the detection of inflammatory diseases. In this study, we utilize an already-existing dataset that includes individual step counts and heart rate data to predict the probability of COVID-19 infection before the onset of symptoms. We train three main model architectures: the Gradient Boosting classifier (GB), CatBoost trees, and TabNet classifier to analyze the physiological data and compare their respective performances. We also add an interpretability layer to our best-performing model, which clarifies prediction results and allows a detailed assessment of effectiveness. Moreover, we created a private dataset by gathering physiological data from Fitbit devices to guarantee reliability and avoid bias.The identical set of models was then applied to this private dataset using the same pre-trained models, and the results were documented. Using the CatBoost tree-based method, our best-performing model outperformed previous studies with an accuracy rate of 85% on the publicly available dataset. Furthermore, this identical pre-trained CatBoost model produced an accuracy of 81% when applied to the private dataset. You will find the source code in the link: https://github.com/OpenUAE-LAB/Covid-19-detection-using-Wearable-data.git.

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“…Despite the promising performance of deep learning models, challenges such as insufficient data, class imbalance, and the need for large datasets for training remain. These issues can impact the generalizability and reliability of the models [3] [4] 2 Context and related works Contact tracing has become a critical component in the fight against the COVID-19 pandemic, and advancements in equipment abilities have played a significant role in improving contact tracing efforts. The types of exposures that qualify someone as a contact can vary but generally include direct physical contact, sharing of a close physical space for a prolonged period, or direct interaction with the biological secretions of the infected individual according to the World Health Organization (WHO).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Approaches for Early Diagnosis and Tracking of COVID-19

LHIADI,

Amaanaoui,

Kaaouachi

et al. 2024

Preprint

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The COVID-19 pandemic has prompted governments and specialists worldwide to collaborate in search of effective solutions and strategies for containment and eventual societal recovery. With advancements in equipment capabilities, remote communications, and on-board/distributed computing, information-based technologies are playing an increasingly critical role in identifying, detecting, and diagnosing potential COVID-19 cases. This study aims to explore factors for early diagnosis, tracking, and identification of COVID-19 spread, focusing on data collection and discussing opportunities for improvement. The study recognizes that deep learning models are well-suited for mitigating the impact of COVID-19, given the availability of a large volume of pandemic data through various technologies and collaborative efforts. While deep learning and big data approaches may not have been extensively implemented or clinically tested, they offer quick responses and valuable insights to medical staff and decision-makers. However, designing deep learning algorithms for COVID-19 presents numerous challenges. The quality and quantity of COVID-19 datasets need further improvement, demanding ongoing efforts from the research community to enhance data quality and reliability.

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An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works

Cited by 17 publications

References 144 publications

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

A tree-based explainable AI model for early detection of Covid-19 using physiological data

Deep Learning-Based Approaches for Early Diagnosis and Tracking of COVID-19

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