Deep Learning Models for Predicting COVID-19 Using Chest X-Ray Images

Muhammad, L. J.; Algehyne, Ebrahem A.; Usman, Sani Sharif; Mohammed, I. A.; Ahmad, Ahmad Sukri; Jibrin, Muhammed Besiru; Malgwi, Yusuf Musa

doi:10.1007/978-3-030-75945-2_6

Cited by 9 publications

(5 citation statements)

References 45 publications

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“…On the other side, DL implementation is providing more support by predicting the clinical findings using CXR and CT scan images [14,15]. For instance, analyzing medical images can provide irregularities in those images by highlighting different spots and predicting infected and normal patients [16]. Therefore, these computing strategies are assisting medical and governmental agencies to generate multiple findings using COVID-19 dataset, for example, severity detection, virus spreading and control, creating policies and guidelines for the communities, helping in medicine and vaccine development.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review

Saleem

AL-Ghamdi

Alassafi

et al. 2022

IJERPH

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COVID-19 is a disease caused by SARS-CoV-2 and has been declared a worldwide pandemic by the World Health Organization due to its rapid spread. Since the first case was identified in Wuhan, China, the battle against this deadly disease started and has disrupted almost every field of life. Medical staff and laboratories are leading from the front, but researchers from various fields and governmental agencies have also proposed healthy ideas to protect each other. In this article, a Systematic Literature Review (SLR) is presented to highlight the latest developments in analyzing the COVID-19 data using machine learning and deep learning algorithms. The number of studies related to Machine Learning (ML), Deep Learning (DL), and mathematical models discussed in this research has shown a significant impact on forecasting and the spread of COVID-19. The results and discussion presented in this study are based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Out of 218 articles selected at the first stage, 57 met the criteria and were included in the review process. The findings are therefore associated with those 57 studies, which recorded that CNN (DL) and SVM (ML) are the most used algorithms for forecasting, classification, and automatic detection. The importance of the compartmental models discussed is that the models are useful for measuring the epidemiological features of COVID-19. Current findings suggest that it will take around 1.7 to 140 days for the epidemic to double in size based on the selected studies. The 12 estimates for the basic reproduction range from 0 to 7.1. The main purpose of this research is to illustrate the use of ML, DL, and mathematical models that can be helpful for the researchers to generate valuable solutions for higher authorities and the healthcare industry to reduce the impact of this epidemic.

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Section: Introductionmentioning

confidence: 99%

Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review

Saleem

AL-Ghamdi

Alassafi

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…As from the literature survey 127 COVID X-Ray images got an accuracy of 87% with 1351 X-ray images achieving 89% of accuracy metrics. Also, Born et al (2020); Mahmud et al (2020); Qayyum et al (2021); Muhammad et al (2022) concluded with an accuracy of 89, 87, 90, and 90% respectively. But when compared to other models from the literature the dataset used in the previous work was trained with a limited number of training images.…”

Section: Resultsmentioning

confidence: 87%

LT-LBP-Based Spatial Texture Feature Extraction with Deep Learning for X-Ray Images

P.,

2024

Journal of Computer Science

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The novel logarithmic transformation of Local Binary Pattern (LT-LBP) for texture feature extraction is used with Deep Learning (DL) models. The Computational complexity in the DL models asymptotically increases with the data sesssst and the depth of the layers in Convolutional Neural Networks (CNN). Pre-processing the images before training the DL model holds a strong impact on the operation speed and throughput. The proposed work encompasses LT-LBP for spatial texture feature extraction and is combined with the DL models. LT-LBP reduces the computation while extracting the texture information from the medical images. The computational cost for the model has been reduced in this study. The experiments were done with various DL models such as inception V3, Dense Net, Mobile Net, Efficient Net (B0-B7) VGG16, and RESNET50, and the results were analyzed. The features were extracted using LT-LBP and given as input to the DL models for classification. The retrospective study was done with 118 COVID-19 X-ray images collected from Chengalpattu Medical College Hospital, Chennai, and the remaining 8,224 images were taken from Kaggle. The combination of LT-LBP with RESNET50 shows better performance when compared to other models with an accuracy of 87%. The Area Under the Curve (AUC) for the proposed model is 88, 83 and 86% for COVID, non-COVID, and pneumonia classifications.

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“…In the study of Muhammad et al by using X‐ray images of patients' chests and ML models, they were able to extract the image features of COVID‐19. 12 , 13 In another study, Gumaei et al used time‐series data on the number of people with COVID‐19 worldwide. They tried to predict patients with the disease using different ML models.…”

Section: Introductionmentioning

confidence: 99%

“…The combination of image datasets and ML has also helped in the diagnosis of COVID‐19. In the study of Muhammad et al by using X‐ray images of patients' chests and ML models, they were able to extract the image features of COVID‐19 12,13 . In another study, Gumaei et al used time‐series data on the number of people with COVID‐19 worldwide.…”

Section: Introductionmentioning

confidence: 99%

Prognosis of COVID‐19 patients using lab tests: A data mining approach

Khounraz

Khodadoost

Gholamzadeh

et al. 2023

Health Science Reports

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Background The rapid prevalence of coronavirus disease 2019 (COVID‐19) has caused a pandemic worldwide and affected the lives of millions. The potential fatality of the disease has led to global public health concerns. Apart from clinical practice, artificial intelligence (AI) has provided a new model for the early diagnosis and prediction of disease based on machine learning (ML) algorithms. In this study, we aimed to make a prediction model for the prognosis of COVID‐19 patients using data mining techniques. Methods In this study, a data set was obtained from the intelligent management system repository of 19 hospitals at Shahid Beheshti University of Medical Sciences in Iran. All patients admitted had shown positive polymerase chain reaction (PCR) test results. They were hospitalized between February 19 and May 12 in 2020, which were investigated in this study. The extracted data set has 8621 data instances. The data include demographic information and results of 16 laboratory tests. In the first stage, preprocessing was performed on the data. Then, among 15 laboratory tests, four of them were selected. The models were created based on seven data mining algorithms, and finally, the performances of the models were compared with each other. Results Based on our results, the Random Forest (RF) and Gradient Boosted Trees models were known as the most efficient methods, with the highest accuracy percentage of 86.45% and 84.80%, respectively. In contrast, the Decision Tree exhibited the least accuracy (75.43%) among the seven models. Conclusion Data mining methods have the potential to be used for predicting outcomes of COVID‐19 patients with the use of lab tests and demographic features. After validating these methods, they could be implemented in clinical decision support systems for better management and providing care to severe COVID‐19 patients.

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Deep Learning Models for Predicting COVID-19 Using Chest X-Ray Images

Cited by 9 publications

References 45 publications

Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review

Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review

LT-LBP-Based Spatial Texture Feature Extraction with Deep Learning for X-Ray Images

Prognosis of COVID‐19 patients using lab tests: A data mining approach

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