A Novel COVID-19 Detection Technique Using Deep Learning Based Approaches

Shehri, Waleed Al; Almalki, Jameel; Mehmood, Rashid; Alsaif, K.; Alshahrani, Saeed M.; Jannah, Najlaa; Alangari, Someah

doi:10.3390/su141912222

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…Machine and deep learning and convolutional neural network algorithms employ image processing techniques in computerized tomography scan and X-ray image processing for abnormality determination and localization (Al Shehri et al, 2022) to enable accurate and timely early disease detection and precise and reliable patient symptom analysis in COVID-19 case detection, diagnosis, and monitoring. Machine and deep learningbased COVID-19 prediction and detection algorithms, big data analytics, and feature engineering and image augmentation techniques can carry out preliminary diagnosis and patient treatment analysis by integrating computerized tomography scan and X-ray image processing.…”

Section: Lucia Michalkova University Of Zilina Slovakiamentioning

confidence: 99%

The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals

Lăzăroiu,

Gedeon,

Rogalska

et al. 2024

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Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis and symptom tracing, optimize intensive care unit admission, and use clinical data to determine patient prioritization and mortality risk, being pivotal in qualitative care provision, reducing medical errors, and increasing patient survival rates, thus diminishing the massive healthcare system burden in relation to severe COVID-19 inpatient stay duration, while increasing operational costs throughout the organizational management of hospitals. Data-driven financial and scenario-based contingency planning, predictive modelling tools, and risk pooling mechanisms should be deployed for additional medical equipment and unforeseen healthcare demand expenses. Purpose of the article: We show that deep and machine learning-based and clinical decision making systems can optimize patient survival likelihood and treatment outcomes with regard to susceptible, infected, and recovered individuals, performing accurate analyses by data modeling based on vital and clinical signs, surveillance data, and infection-related biomarkers, and furthering hospital facility optimization in terms of intensive care unit bed allocation. Methods: The review software systems employed for article screening and quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, and SRDR. Findings & value added: Deep and machine learning-based clinical decision support tools can forecast COVID-19 spread, confirmed cases, and infection and mortality rates for data-driven appropriate treatment and resource allocations in effective therapeutic and diagnosis protocol development, by determining suitable measures and regulations and by using symptoms and comorbidities, vital signs, clinical and laboratory data and medical records across intensive care units, impacting the healthcare financing infrastructure. As a result of heightened use of personal protective equipment, hospital pharmacy and medication, outpatient treatment, and medical supplies, revenue loss and financial vulnerability occur, also due to expenses related to hiring additional staff and to critical resource expenditures. Hospital costs for COVID-19 medical care, screening, treatment capacity expansion, and personal protective equipment can lead to further financial losses while affecting COVID-19 frontline hospital workers and patients.

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Section: Lucia Michalkova University Of Zilina Slovakiamentioning

confidence: 99%

The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals

Lăzăroiu,

Gedeon,

Rogalska

et al. 2024

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“…Additionally, the obtained results for the four classes of the SCH dataset, namely normal, pneumonia, pneumothorax, and TB revealed a validation accuracy of 82.20%. To analyze CT scans and X-ray images, the researchers in [ 41 ] used pre-implemented instances of a CNN and Darknet. CNN and Darknet with image processing algorithms enable the analysis, identification, and localization of anomalies in CT scans and X-ray images.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous Super-Resolution and Classification of Lung Disease Scans

et al. 2023

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Acute lower respiratory infection is a leading cause of death in developing countries. Hence, progress has been made for early detection and treatment. There is still a need for improved diagnostic and therapeutic strategies, particularly in resource-limited settings. Chest X-ray and computed tomography (CT) have the potential to serve as effective screening tools for lower respiratory infections, but the use of artificial intelligence (AI) in these areas is limited. To address this gap, we present a computer-aided diagnostic system for chest X-ray and CT images of several common pulmonary diseases, including COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, lung opacity, and various types of carcinoma. The proposed system depends on super-resolution (SR) techniques to enhance image details. Deep learning (DL) techniques are used for both SR reconstruction and classification, with the InceptionResNetv2 model used as a feature extractor in conjunction with a multi-class support vector machine (MCSVM) classifier. In this paper, we compare the proposed model performance to those of other classification models, such as Resnet101 and Inceptionv3, and evaluate the effectiveness of using both softmax and MCSVM classifiers. The proposed system was tested on three publicly available datasets of CT and X-ray images and it achieved a classification accuracy of 98.028% using a combination of SR and InceptionResNetv2. Overall, our system has the potential to serve as a valuable screening tool for lower respiratory disorders and assist clinicians in interpreting chest X-ray and CT images. In resource-limited settings, it can also provide a valuable diagnostic support.

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“…Additionally, deep learning frameworks have not been fully utilized to distinguish COVID and TB using X-beam images. The authors in [18] proposed an Internet of Things (IoT) based screening system to detect face masks using the VGG16 deep learning model.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of Tuberculosis and Coronavirus Patients Using Hybrid Deep Learning Models

Ghamdi¹

2023

Computers, Materials &Amp; Continua

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Considerable resources, technology, and efforts are being utilized worldwide to eradicate the coronavirus. Although certain measures taken to prevent the further spread of the disease have been successful, efforts to completely wipe out the coronavirus have been insufficient. Coronavirus patients have symptoms similar to those of chest Tuberculosis (TB) or pneumonia patients. Chest tuberculosis and coronavirus are similar because both diseases affect the lungs, cause coughing and produce an irregular respiratory system. Both diseases can be confirmed through X-ray imaging. It is a difficult task to diagnose COVID-19, as coronavirus testing kits are neither excessively available nor very reliable. In addition, specially trained staff and specialized equipment in medical laboratories are needed to carry out a coronavirus test. However, most of the staff is not fully trained, and several laboratories do not have special equipment to perform a coronavirus test. Therefore, hospitals and medical staff are under stress to meet necessary workloads. Most of the time, these staffs confuse the tuberculosis or pneumonia patient with a coronavirus patient, as these patients present similar symptoms. To meet the above challenges, a comprehensive solution based on a deep learning model has been proposed to distinguish COVID-19 patients from either tuberculosis patients or healthy people. The framework contains a fusion of Visual Geometry Group from Oxford (VGG16) and Residual Network (ResNet18) algorithms as VGG16 contains robust convolutional layers, and Resnet18 is a good classifier. The proposed model outperforms other machine learning and deep learning models as more than 94% accuracy for multiclass identification has been achieved.

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A Novel COVID-19 Detection Technique Using Deep Learning Based Approaches

Cited by 7 publications

References 29 publications

The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals

The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals

Simultaneous Super-Resolution and Classification of Lung Disease Scans

Identification of Tuberculosis and Coronavirus Patients Using Hybrid Deep Learning Models

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