Pictures of X-Rays Displayed in Monitors for Deep Learning-Based COVID-19 Screening: Implications for Mobile Application Development

Luz, Eduardo; Moreira, Gladston; Gomes, Caio; Viana, Larissa Vanessa Machado; Silva, Rodrigo

doi:10.21203/rs.3.rs-37908/v1

Cited by 4 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are also some other noticeable challenges in this regard. Experiments performed in [149] have shown that the quality of images in this way is not adequate for managing the smartphone based applications. It is also challenging to get accurate and relevant speech data for developing deep learning models in terms of social distancing norms.…”

Section: Detecting and Diagnosing Covid-19 Patients With No Or Mild C...mentioning

confidence: 99%

Deep-Learning-Based COVID-19 Detection: Challenges and Future Directions

Arshad

Khan

Azam

et al. 2023

IEEE Trans. Artif. Intell.

View full text Add to dashboard Cite

is an ecumenical pandemic that has affected the whole world drastically by raising a global calamitous situation. Due to this pernicious disease, millions of people have lost their lives. The scientists are still far from knowing how to tackle the coronavirus due to its multiple mutations found around the globe. Standard testing technique called Polymerase Chain Reaction (PCR) for the clinical diagnosis of COVID-19 is expensive and time consuming. However, to assist specialists and radiologists in COVID-19 detection and diagnosis, deep learning plays an important role. Many research efforts have been done that leverage deep learning techniques and technologies for the identification or categorization of COVID-19 positive patients, and these techniques are proved to be a powerful tool that can automatically detect or diagnose COVID-19 cases. In this paper, we identify significant challenges regarding deep learning-based systems and techniques that use different medical imaging modalities, including Cough and Breadth, Chest X-ray, and Computer Tomography (CT) to combat COVID-19 outbreak. We also pinpoint important research questions for each category of challenges. The challenges highlighted in this paper will call an attention to the noticeable weaknesses and problems in the existing deep learning based COVID-19 detection systems and techniques. Moreover, the research questions for each challenge will guide the researchers to come up with novel solutions in COVID-19 detection.

show abstract

Section: Detecting and Diagnosing Covid-19 Patients With No Or Mild C...mentioning

confidence: 99%

Deep-Learning-Based COVID-19 Detection: Challenges and Future Directions

Arshad

Khan

Azam

et al. 2023

IEEE Trans. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…They found that the sensitivity for COVID-19/pneumonia diagnosis was improved based on the mlCXR image interpretation. While one study applied the convolutional neural network model for COVID-19 diagnosis based on the CXR images collected from the Kaggle dataset [19], another study demonstrated that deep learning models failed to classify the CXR images taken from smartphones [20]. Therefore, it is essential to check the sources of images before inputting the images into deep learning models.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of COVID-19 Using a Stacked Denoising Convolutional Autoencoder

Dhahri¹,

Rabhi²,

Chelbi³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The exponential increase in new coronavirus disease 2019 cases and deaths has made COVID-19 the leading cause of death in many countries. Thus, in this study, we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images. A stacked denoising convolutional autoencoder (SDCA) model was proposed to classify X-ray images into three classes: normal, pneumonia, and COVID-19. The SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy images. The proposed model's architecture mainly composed of eight autoencoders, which were fed to two dense layers and SoftMax classifiers. The proposed model was evaluated with 6356 images from the datasets from different sources. The experiments and evaluation of the proposed model were applied to an 80/20 training/validation split and for five cross-validation data splitting, respectively. The metrics used for the SDCA model were the classification accuracy, precision, sensitivity, and specificity for both schemes. Our results demonstrated the superiority of the proposed model in classifying X-ray images with high accuracy of 96.8%. Therefore, this model can help physicians accelerate COVID-19 diagnosis.

show abstract

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

160

View full text Add to dashboard Cite

show abstract

Pictures of X-Rays Displayed in Monitors for Deep Learning-Based COVID-19 Screening: Implications for Mobile Application Development

Cited by 4 publications

References 9 publications

Deep-Learning-Based COVID-19 Detection: Challenges and Future Directions

Deep-Learning-Based COVID-19 Detection: Challenges and Future Directions

Automatic Detection of COVID-19 Using a Stacked Denoising Convolutional Autoencoder

Applications of artificial intelligence in battling against covid-19: A literature review

Contact Info

Product

Resources

About