A new COVID-19 detection method from human genome sequences using CpG island features and KNN classifier DOI Creative Commons
Hilal Arslan, Hasan Arslan

Engineering Science and Technology an International Journal, Journal Year: 2021, Volume and Issue: 24(4), P. 839 - 847

Published: Jan. 12, 2021

Various viral epidemics have been detected such as the severe acute respiratory syndrome coronavirus and Middle East in last two decades. The disease 2019 (COVID-19) is a pandemic caused by novel betacoronavirus called coronavirus-2 (SARS-CoV-2). After rapid spread of COVID-19, many researchers investigated diagnosis treatment for this terrifying quickly. Identifying COVID-19 from other types coronaviruses difficult problem due to their genetic similarity. In study, we propose new efficient detection method based on K-nearest neighbors (KNN) classifier using complete genome sequences human dataset recorded Novel Coronavirus Resource. We also describe features CpG island that efficiently detect cases. Thus, including approximately 30,000 nucleotides can be represented only real numbers. KNN simple effective non-parametric technique solving classification problems. However, performance depends distance measure used. perform 19 metrics five categories improve algorithm. Some parameters are computed evaluate proposed method. achieves 98.4% precision, 99.2% recall, 98.8% F-measure, accuracy few seconds when any L1 type metric used KNN.

Language: Английский

Densely connected convolutional networks-based COVID-19 screening model DOI Creative Commons
Dilbag Singh, Vijay Kumar, Manjit Kaur

et al.

Applied Intelligence, Journal Year: 2021, Volume and Issue: 51(5), P. 3044 - 3051

Published: Feb. 7, 2021

Language: Английский

Citations

115
Complex features extraction with deep learning model for the detection of COVID19 from CT scan images using ensemble based machine learning approach DOI

Md. Robiul Islam,

Md. Nahiduzzaman

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 195, P. 116554 - 116554

Published: Feb. 4, 2022

Language: Английский

Citations

106
Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model DOI Open Access
Mana Saleh Al Reshan, Kanwarpartap Singh Gill, Vatsala Anand

et al.

Healthcare, Journal Year: 2023, Volume and Issue: 11(11), P. 1561 - 1561

Published: May 26, 2023

Pneumonia has been directly responsible for a huge number of deaths all across the globe. shares visual features with other respiratory diseases, such as tuberculosis, which can make it difficult to distinguish between them. Moreover, there is significant variability in way chest X-ray images are acquired and processed, impact quality consistency images. This challenging develop robust algorithms that accurately identify pneumonia types Hence, need robust, data-driven trained on large, high-quality datasets validated using range imaging techniques expert radiological analysis. In this research, deep-learning-based model demonstrated differentiating normal severe cases pneumonia. complete proposed system total eight pre-trained models, namely, ResNet50, ResNet152V2, DenseNet121, DenseNet201, Xception, VGG16, EfficientNet, MobileNet. These models were simulated two having 5856 112,120 X-rays. The best accuracy obtained MobileNet values 94.23% 93.75% different datasets. Key hyperparameters including batch sizes, epochs, optimizers have considered during comparative interpretation these determine most appropriate model.

Language: Английский

Citations

106
COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network DOI Creative Commons
Tawsifur Rahman, Alex Akinbi, Muhammad E. H. Chowdhury

et al.

Health Information Science and Systems, Journal Year: 2022, Volume and Issue: 10(1)

Published: Jan. 19, 2022

Abstract The reliable and rapid identification of the COVID-19 has become crucial to prevent spread disease, ease lockdown restrictions reduce pressure on public health infrastructures. Recently, several methods techniques have been proposed detect SARS-CoV-2 virus using different images data. However, this is first study that will explore possibility deep convolutional neural network (CNN) models from electrocardiogram (ECG) trace images. In work, other cardiovascular diseases (CVDs) were detected deep-learning techniques. A dataset ECG consisting 1937 five distinct categories, such as normal, COVID-19, myocardial infarction (MI), abnormal heartbeat (AHB), recovered (RMI) used in study. Six CNN (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, MobileNetv2) investigate three classification schemes: (i) two-class (normal vs COVID-19); (ii) three-class (normal, CVDs), finally, (iii) five-class MI, AHB, RMI). For classification, Densenet201 outperforms networks with an accuracy 99.1%, 97.36%, respectively; while for InceptionV3 others 97.83%. ScoreCAM visualization confirms are learning relevant area Since method uses which can be captured by smartphones readily available facilities low-resources countries, help faster computer-aided diagnosis cardiac abnormalities.

Language: Английский

Citations

88
A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications DOI Open Access

Nirmala Devi Kathamuthu,

S. Shanthi,

Hoang-Quynh Le

et al.

Advances in Engineering Software, Journal Year: 2022, Volume and Issue: 175, P. 103317 - 103317

Published: Oct. 24, 2022

Language: Английский

Citations

74
Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning DOI Open Access
Navid Ghassemi, Afshin Shoeibi, Marjane Khodatars

et al.

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 144, P. 110511 - 110511

Published: June 15, 2023

Language: Английский

Citations

47
Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment DOI Creative Commons
Md. Mahadi Hasan, Muhammad Usama Islam, Muhammad Jafar Sadeq

et al.

Sensors, Journal Year: 2023, Volume and Issue: 23(1), P. 527 - 527

Published: Jan. 3, 2023

Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in real world domain. intelligence, driving force current technological revolution, been used many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, most importantly healthcare sector. With rise COVID-19 pandemic, several prediction detection methods using artificial have employed to understand, forecast, handle, curtail ensuing threats. In this study, recent related publications, methodologies medical reports were investigated purpose studying intelligence's role pandemic. This study presents comprehensive review specific attention machine learning, deep image processing, object detection, segmentation, few-shot learning studies that utilized tasks COVID-19. particular, genetic analysis, clinical data sound biomedical classification, socio-demographic anomaly health monitoring, personal protective equipment (PPE) observation, social control, patients' mortality risk approaches forecast threatening factors demonstrates artificial-intelligence-based algorithms integrated into Internet Things wearable devices quite effective efficient forecasting insights which actionable through wide usage. The results produced by prove is promising arena can be applied for disease prognosis, forecasting, drug discovery, development sector on global scale. We indeed played important helping fight against COVID-19, insightful knowledge provided here could extremely beneficial practitioners experts domain implement systems curbing next pandemic or disaster.

Language: Английский

Citations

46
A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review DOI Creative Commons
Sunil Kumar,

Harish Kumar,

Gyanendra Kumar

et al.

BMC Medical Imaging, Journal Year: 2024, Volume and Issue: 24(1)

Published: Feb. 1, 2024

Abstract Background Lung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in world. Medical research has identified pneumonia, lung cancer, Corona Virus Disease 2019 (COVID-19) as prominent diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission (PET) others, primarily employed medical assessments because they provide computed data that can be utilized input datasets for computer-assisted diagnostic systems. used to develop evaluate machine learning (ML) methods analyze predict diseases. Objective This review analyzes ML paradigms, modalities' utilization, recent developments Furthermore, also explores various available publically being Methods The well-known databases academic studies have been subjected peer review, namely ScienceDirect, arXiv, IEEE Xplore, MDPI, many more, were search relevant articles. Applied keywords combinations procedures with primary considerations such COVID-19, ML, convolutional neural networks (CNNs), transfer learning, ensemble learning. Results finding indicates X-ray preferred detecting while CT scan predominantly favored cancer. COVID-19 detection, datasets. analysis reveals X-rays scans surpassed all other techniques. It observed using CNNs yields a high degree accuracy practicability identifying Transfer complementary techniques facilitate analysis. is metric assessment.

Language: Английский

Citations

21
“Fast deep learning computer-aided diagnosis of COVID-19 based on digital chest x-ray images” DOI Creative Commons
Mugahed A. Al-antari, Cam-Hao Hua, Jaehun Bang

et al.

Applied Intelligence, Journal Year: 2020, Volume and Issue: 51(5), P. 2890 - 2907

Published: Nov. 28, 2020

Language: Английский

Citations

105
COVID-19: Automatic detection from X-ray images by utilizing deep learning methods DOI Open Access

Bhawna Nigam,

Ayan Nigam,

Rahul Kumar Jain

et al.

Expert Systems with Applications, Journal Year: 2021, Volume and Issue: 176, P. 114883 - 114883

Published: March 18, 2021

Language: Английский

Citations

92