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COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers

SENSORS (2022)

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Article Computer Science, Artificial Intelligence

Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

Debabrata Dansana et al.

Summary: The COVID-19 pandemic, which began in December 2019 in China, has rapidly spread worldwide and infected over ten million people. This study explores the binary classification of pneumonia using convolution neural networks on X-ray and CT scan images. The findings show that fine-tuned versions of VGG-19 and Inception_V2 models demonstrate high accuracy rates.

SOFT COMPUTING (2023)

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Article Computer Science, Artificial Intelligence

COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images

Alaa S. Al-Waisy et al.

Summary: The outbreaks of the COVID-19 epidemic have increased the pressure on healthcare and medical systems worldwide. Chest radiography imaging has been shown to be an effective screening technique for diagnosing the COVID-19 epidemic. To reduce pressure on radiologists, a hybrid deep learning framework called COVID-CheXNet has been developed for fast and accurate diagnosis of COVID-19 virus in chest X-ray images. The system achieved high detection accuracy and efficiency, making it a potential tool for real clinical centers.

SOFT COMPUTING (2023)

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Article Computer Science, Artificial Intelligence

Efficient deep learning approach for augmented detection of Coronavirus disease

Ahmed Sedik et al.

Summary: This paper proposes a COVID-19 detection system based on deep learning, utilizing CNN and ConvLSTM. By using CT and X-ray image datasets, the proposed modalities achieved 100% accuracy and F1 score. The results indicate that these deep learning modalities can be adopted for quick COVID-19 screening.

NEURAL COMPUTING & APPLICATIONS (2022)

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Article Computer Science, Information Systems

COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning

Arman Haghanifar et al.

Summary: This study focuses on efficiently detecting imaging features of novel coronavirus pneumonia using deep convolutional neural networks. The proposed COVID-CXNet model is capable of precise localization based on relevant and meaningful features, which is a step towards a fully automated and robust COVID-19 detection system.

MULTIMEDIA TOOLS AND APPLICATIONS (2022)

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Article Biochemistry & Molecular Biology

Using X-ray images and deep learning for automated detection of coronavirus disease

Khalid El Asnaoui et al.

Summary: The study compared the use of deep learning models for the detection and classification of coronavirus pneumonia, finding that Inception_ResNetV2 and DenseNet201 performed better than other models used in the research.

JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS (2021)

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Article Computer Science, Artificial Intelligence

COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

Tahmina Zebin et al.

Summary: The study utilized transfer learning to classify and detect COVID-19 chest X-ray images with an accuracy of up to 96.8%. Additionally, a generative adversarial network was used to generate and enhance the COVID-19 class, while a gradient class activation mapping technique was implemented for result interpretation.

APPLIED INTELLIGENCE (2021)

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Article Computer Science, Artificial Intelligence

Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices

Sakshi Ahuja et al.

Summary: In this research, transfer learning from CT scan images is used to detect COVID-19 with different pre-trained architectures, the results show that the ResNet18 model offers better classification accuracy compared to other alternatives.

APPLIED INTELLIGENCE (2021)

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Article Computer Science, Artificial Intelligence

COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble

Muammer Turkoglu

Summary: The novel coronavirus has spread rapidly worldwide, resulting in a global pandemic that has posed a significant threat to human health. The limitations and time constraints of COVID-19 diagnostic tests have led to the utilization of lung X-ray images as a faster and more reliable method for diagnosis. The proposed COVIDetectioNet model, utilizing deep features and machine learning techniques, has shown a high accuracy of 99.18% in diagnosing COVID-19 from X-ray images, outperforming previous studies.

APPLIED INTELLIGENCE (2021)

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Article Computer Science, Artificial Intelligence

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Asmaa Abbas et al.

Summary: This paper validates a deep CNN model called DeTraC, which utilizes a class decomposition mechanism to handle irregularities in medical image datasets. Experimental results demonstrate the high accuracy of DeTraC in detecting COVID-19 X-ray images.

APPLIED INTELLIGENCE (2021)

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Article Computer Science, Artificial Intelligence

Deep learning based detection and analysis of COVID-19 on chest X-ray images

Rachna Jain et al.

Summary: Covid-19 is a rapidly spreading viral disease that affects both humans and animals. Deep learning techniques can provide useful analysis of chest x-ray images to aid in the screening of Covid-19. The Xception model shows the highest accuracy in detecting chest x-ray images compared to other models.

APPLIED INTELLIGENCE (2021)

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Article Computer Science, Artificial Intelligence

Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks

Narinder Singh Punn et al.

Summary: The novel coronavirus 2019 (COVID-19) presents a respiratory syndrome resembling pneumonia, with the current diagnostic procedure being less sensitive at the initial stage. To improve diagnosis efficiency, publicly available datasets of corona positive patients are being utilized for faster and automated diagnosis using deep learning approaches. Various state-of-the-art deep learning models are being fine-tuned using random oversampling and weighted class loss function techniques for improved classification of COVID-19 cases in chest X-ray images. NASNetLarge shows better performance compared to other architectures, as demonstrated through evaluation metrics such as accuracy, precision, recall, loss, and area under the curve (AUC).

APPLIED INTELLIGENCE (2021)

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Article Engineering, Electrical & Electronic

Classification of Coronavirus (COVID-19) fromX-rayandCTimages using shrunken features

Saban Ozturk et al.

Summary: The study highlights the importance of using machine learning methods to detect viral epidemics by analyzing X-ray and CT images for making an effective diagnosis of COVID-19. Utilizing shallow image augmentation and the Synthetic Minority Over-sampling Technique algorithm proved to be effective for handling deficient and unbalanced datasets in this research.

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY (2021)

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Article Computer Science, Artificial Intelligence

Attention-based VGG-16 model for COVID-19 chest X-ray image classification

Chiranjibi Sitaula et al.

Summary: The study proposes a novel approach using an attention-based deep learning model with VGG-16 for diagnosing COVID-19. Experimental results demonstrate the promising performance of the method in COVID-19 CXR image classification.

APPLIED INTELLIGENCE (2021)

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Article Computer Science, Artificial Intelligence

The ensemble deep learning model for novel COVID-19 on CT images

Zhou Tao et al.

Summary: This study proposes an ensemble deep learning model for rapid detection of COVID-19 from CT images, achieving higher accuracy and sensitivity compared to individual classifiers. This approach can better meet the requirements for rapid detection of the novel coronavirus disease COVID-19.

APPLIED SOFT COMPUTING (2021)

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Article Mathematics, Interdisciplinary Applications

CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images

Emtiaz Hussain et al.

Summary: A novel CNN model called CoroDet was proposed for automatic detection of COVID-19 using raw chest X-ray and CT scan images in this study. The model outperformed existing techniques in terms of classification accuracy, providing a solution to the issue of scarcity of COVID-19 testing kits.

CHAOS SOLITONS & FRACTALS (2021)

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Review Biology

A scoping review of transfer learning research on medical image analysis using ImageNet

Mohammad Amin Morid et al.

Summary: This study reviewed the application of transfer learning with convolutional neural networks in medical image analysis, identifying the most common anatomical areas studied and the use of different models for analysis in different anatomical areas. Findings can guide future research approaches and highlight research gaps in the field.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

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Article Computer Science, Artificial Intelligence

FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection

Mohamed Abdel-Basset et al.

Summary: The newly discovered coronavirus pneumonia poses major challenges to diagnosis and disease quantification, and deep learning techniques coupled with Few-Shot Learning paradigms present an innovative semi-supervised approach for accurate segmentation of COVID-19 infection in limited annotated data. The proposed dual-path deep-learning architecture with adaptive recombination and recalibration module enables effective collaboration between paths and overcomes the limitation of lack of large numbers of COVID-19 CT scans, providing a general framework for lung disease diagnosis in limited data situations.

KNOWLEDGE-BASED SYSTEMS (2021)

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Article Biochemical Research Methods

Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images

Ying Song et al.

Summary: The emergence of COVID-19 has led to the urgent need for accurate diagnoses through CT scans. Researchers have developed a deep learning-based CT diagnosis system to accurately identify COVID-19 patients, achieving high levels of accuracy and reliability.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2021)

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Article Chemistry, Analytical

Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning

Hammam Alshazly et al.

Summary: This paper utilizes deep learning models and visualization techniques to conduct experiments on SARS-CoV-2 CT-scan and COVID19-CT datasets, achieving rapid and accurate diagnosis of COVID-19 infected individuals.

SENSORS (2021)

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Article Computer Science, Artificial Intelligence

A critic evaluation of methods for COVID-19 automatic detection from X-ray images

Gianluca Maguolo et al.

Summary: By manipulating X-Ray images, a more fair COVID-19 diagnostic testing protocol can be achieved. Neural networks may learn patterns in the dataset that are not relevant to COVID-19. Creating a fair testing protocol is a challenging task.

INFORMATION FUSION (2021)

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Article Multidisciplinary Sciences

Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients

Nathalie Lassau et al.

Summary: The authors have developed a multimodal severity score including clinical and imaging features, which has significantly improved prognostic performance in two validation datasets compared to previous scores.

NATURE COMMUNICATIONS (2021)

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Article Computer Science, Artificial Intelligence

Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19

Abdullahi Umar Ibrahim et al.

Summary: The COVID-19 pandemic has created a global crisis, sparking the need for an accurate, fast, and affordable detection method. This study proposes the use of a deep learning approach based on a pretrained AlexNet model for classifying COVID-19, non-COVID-19 viral pneumonia, bacterial pneumonia, and normal CXR scans.

COGNITIVE COMPUTATION (2021)

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Article Mathematical & Computational Biology

A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images

Jawad Rasheed et al.

Summary: Researchers investigated the potential of machine learning methods for automatic diagnosis of COVID-19 from X-ray images, using logistic regression and convolutional neural networks for classification. They improved accuracy through techniques such as principal component analysis and generative adversarial network.

INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES (2021)

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Article Computer Science, Artificial Intelligence

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray

Anunay Gupta et al.

Summary: The newly discovered coronavirus (COVID-19) has caused a global pandemic, but artificial intelligence models play a significant role in medical diagnosis. The proposed model successfully detects COVID-19 and pneumonia with high accuracy, benefiting humanity greatly during this age of Quarantine.

APPLIED SOFT COMPUTING (2021)

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Article Engineering, Biomedical

Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study

Soumya Ranjan Nayak et al.

Summary: The emergence of COVID-19 in December 2019 has caused significant damage, with approximately five million confirmed cases worldwide. To address the insufficient testing capacity and time-consuming manual testing methods, an automated early diagnosis system using DL algorithms is proposed in this study. ResNet-34 demonstrated the best performance with an accuracy of 98.33% for classifying COVID-19 from normal cases, suggesting its potential for early detection of the virus.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

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Article Biology

Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images

Tawsifur Rahman et al.

Summary: Computer-aided diagnosis for fast and reliable detection of COVID-19 using a large X-ray dataset, various image enhancement techniques, and deep learning models has shown promising results in this study.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

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Article Computer Science, Artificial Intelligence

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Tej Bahadur Chandra et al.

Summary: This study introduces an automatic COVID screening (ACoS) system for identifying nCOVID-19 infected patients, which utilizes radiomic texture descriptors and a majority vote based classifier ensemble of five benchmark supervised classification algorithms. The system shows promising performance in the validation phase, with statistically significant results confirmed through Friedman post-hoc multiple comparisons and z-test statistics.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

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Article Computer Science, Artificial Intelligence

Deep learning approaches for COVID-19 detection based on chest X-ray images

Aras M. Ismael et al.

Summary: COVID-19 is a novel virus that has led to a global pandemic, with daily increases in cases and deaths. Deep learning approaches, including deep feature extraction and fine-tuning of pretrained convolutional neural networks, show potential in detecting COVID-19 based on chest X-ray images.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

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Article Chemistry, Analytical

Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images

Lucas O. Teixeira et al.

Summary: The study demonstrated the impact of lung segmentation in COVID-19 identification using CXR images, achieving good Jaccard distance and Dice coefficient for segmentation. It investigated the generalization of COVID-19 from images created from different sources, finding a strong bias introduced by underlying factors from different sources even after segmentation.

SENSORS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet

Adnan Saood et al.

Summary: This study utilizes SegNet and U-NET deep learning networks for the classification of infected tissue in CT lung images, with SegNet showing superior performance in distinguishing infected/non-infected tissues and U-NET performing better as a multi-class segmentor.

BMC MEDICAL IMAGING (2021)

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Article Biology

Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases

Dina M. Ibrahim et al.

Summary: COVID-19 has been declared a pandemic, early detection is crucial to protect infected individuals. A deep learning model for diagnosing COVID-19, pneumonia, and lung cancer from chest x-ray and CT images was proposed, with the VGG19+CNN model showing the best performance in experiments.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

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Article Computer Science, Artificial Intelligence

Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network

Amit Kumar Das et al.

Summary: This study introduces a solution based on deep convolutional neural networks to detect COVID-19 positive patients using chest X-ray images. Multiple CNN models are utilized and combined through a weighted average ensembling technique for prediction.

PATTERN ANALYSIS AND APPLICATIONS (2021)

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Article Computer Science, Artificial Intelligence

Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks

Ali Narin et al.

Summary: The 2019 novel coronavirus disease has rapidly spread worldwide, with nearly 101,917,147 cases reported, leading to a limited availability of COVID-19 test kits in hospitals. To address this, an automatic detection system using pre-trained convolutional neural network-based models was proposed for detecting coronavirus pneumonia-infected patients. Among the five models tested, ResNet50 demonstrated the highest accuracy in classifying COVID-19 patients from X-ray images.

PATTERN ANALYSIS AND APPLICATIONS (2021)

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Article Multidisciplinary Sciences

COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images

Abolfazl Zargari Khuzani et al.

Summary: The study explores the use of machine learning classifiers to differentiate COVID-19 from other pneumonia cases based on CXR images, suggesting that it can be a valuable tool for rapid triage and diagnosis.

SCIENTIFIC REPORTS (2021)

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Article Engineering, Biomedical

A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images

Guangyu Wang et al.

Summary: An automated deep-learning pipeline was developed for standardizing chest X-ray images, visualizing lesions, and diagnosing diseases, which can identify viral pneumonia caused by COVID-19, assess its severity, and distinguish it from other types of pneumonia. The system showed high discriminative abilities across different types of pneumonia and performed comparably to senior radiologists, indicating its potential to support clinical decision-making and facilitate early intervention.

NATURE BIOMEDICAL ENGINEERING (2021)

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Article Engineering, Biomedical

Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: A machine learning based approach

Sara Hosseinzadeh Kassani et al.

Summary: COVID-19 is highly transmittable and pathogenic, with no approved antiviral drug or vaccine for treatment. Computer-aided diagnosis systems can assist in early detection of COVID-19 abnormalities. Different deep learning-based feature extraction frameworks were compared for automatic COVID-19 classification in this study.

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING (2021)

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Article Engineering, Biomedical

A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset

Mohammad Rahimzadeh et al.

Summary: This paper presents a high-speed and accurate fully-automated method for detecting COVID-19 from chest CT scan images. By introducing a new dataset and algorithm, the system achieves significant improvements in classification accuracy and speed.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

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Article Engineering, Electrical & Electronic

Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging

Rajesh Kumar et al.

Summary: This paper proposes a framework for diagnosing COVID-19 using blockchain-based federated learning. By addressing issues such as data normalization, Capsule Network-based segmentation and classification, and collaborative global model training, the proposed method improves the performance of COVID-19 patient detection.

IEEE SENSORS JOURNAL (2021)

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Article Health Care Sciences & Services

Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19

Karim Hammoudi et al.

Summary: COVID-19, initially presenting flu-like symptoms and causing pneumonia, has led to a global pandemic. This study explores the use of deep learning to automatically analyze chest X-ray images for screening and diagnosing COVID-19 patients.

JOURNAL OF MEDICAL SYSTEMS (2021)

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Article Medical Informatics

Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning?

Tuan D. Pham

Summary: This study investigated the fine tuning of pretrained convolutional neural networks for COVID-19 classification using chest X-rays. Three pretrained CNNs achieved high classification results without data augmentation, with AlexNet, GoogleNet, and SqueezeNet requiring the least training time among pretrained DL models. These findings contribute to the urgent need for deploying AI tools in the public domain for rapid implementation during the pandemic.

HEALTH INFORMATION SCIENCE AND SYSTEMS (2021)

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Article Automation & Control Systems

Automatic Detection of COVID-19 Infection Using Chest X-Ray Images Through Transfer Learning

Elene Firmeza Ohata et al.

Summary: The new coronavirus has become a global pandemic, infecting over 1 million people and causing more than 50 thousand deaths. A new method for automatically detecting COVID-19 infection based on chest X-ray images has been proposed and shown to be efficient in detecting COVID-19 in X-ray images.

IEEE-CAA JOURNAL OF AUTOMATICA SINICA (2021)

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Article Computer Science, Artificial Intelligence

A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images

K. Shankar et al.

Summary: The COVID-19 pandemic is escalating rapidly with limited access to rapid test kits, prompting researchers to explore new methods using AI techniques and radiological imaging for more accurate disease diagnosis and classification. The proposed FM-HCF-DLF model demonstrated superior performance in experimental validation, with maximum sensitivity of 93.61%, specificity of 94.56%, precision of 94.85%, accuracy of 94.08%, F score of 93.2%, and kappa value of 93.5%.

COMPLEX & INTELLIGENT SYSTEMS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Diagnosis of COVID-19 using CT scan images and deep learning techniques

Vruddhi Shah et al.

Summary: Early and accurate diagnosis of COVID-19 is crucial for pandemic control. Utilizing deep learning techniques to analyze CT scan images can assist doctors in quickly screening for COVID-19.

EMERGENCY RADIOLOGY (2021)

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Article Mathematics, Interdisciplinary Applications

Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet

Harsh Panwar et al.

CHAOS SOLITONS & FRACTALS (2020)

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Article Physics, Multidisciplinary

Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features

Ali M. Hasan et al.

ENTROPY (2020)

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Article Respiratory System

A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis

Shuo Wang et al.

EUROPEAN RESPIRATORY JOURNAL (2020)

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Article Orthopedics

Deep learning COVID-19 detection bias: accuracy through artificial intelligence

Shashank Vaid et al.

INTERNATIONAL ORTHOPAEDICS (2020)

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Article Engineering, Biomedical

Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases

Ioannis D. Apostolopoulos et al.

JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING (2020)

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Article Multidisciplinary Sciences

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

Mohamed Loey et al.

SYMMETRY-BASEL (2020)

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Article Mathematics, Interdisciplinary Applications

A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images

Harsh Panwar et al.

CHAOS SOLITONS & FRACTALS (2020)

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Article Automation & Control Systems

An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based COVID-19 detection method using chest X-ray image

Turker Tuncer et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2020)

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Article Biology

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

Mesut Togacar et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2020)

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Article Biology

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

Ali Abbasian Ardakani et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2020)

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Article Biology

CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization

Tanvir Mahmud et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2020)

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Article Biology

Automated detection of COVID-19 cases using deep neural networks with X-ray images

Tulin Ozturk et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software

Hai-tao Zhang et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Deep learning -based multi -view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study

Xiangjun Wu et al.

EUROPEAN JOURNAL OF RADIOLOGY (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

From community-acquired pneumonia to COVID-19: a deep learning-based method for quantitative analysis of COVID-19 on thick-section CT scans

Zhang Li et al.

EUROPEAN RADIOLOGY (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images

Qianqian Ni et al.

EUROPEAN RADIOLOGY (2020)

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Article Computer Science, Interdisciplinary Applications

Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

Yujin Oh et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2020)

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Article Chemistry, Multidisciplinary

Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images

Javier Civit-Masot et al.

APPLIED SCIENCES-BASEL (2020)

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Article Engineering, Multidisciplinary

A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia

Xiaowei Xu et al.

ENGINEERING (2020)

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Article Medicine, General & Internal

Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging

Seung Hoon Yoo et al.

FRONTIERS IN MEDICINE (2020)

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Article Health Care Sciences & Services

COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation

Hoon Ko et al.

JOURNAL OF MEDICAL INTERNET RESEARCH (2020)

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Article Engineering, Biomedical

Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier

Bejoy Abraham et al.

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING (2020)

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Article Engineering, Biomedical

A deep learning approach to detect Covid-19 coronavirus with X-Ray images

Govardhan Jain et al.

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING (2020)

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Article Medicine, Research & Experimental

COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images

Ferhat Ucar et al.

MEDICAL HYPOTHESES (2020)

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Article Computer Science, Interdisciplinary Applications

COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios

Rodolfo M. Pereira et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2020)

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Article Virology

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

Ahmed Sedik et al.

VIRUSES-BASEL (2020)

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Article Instruments & Instrumentation

Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches

Md Mamunur Rahaman et al.

JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY (2020)

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Review Computer Science, Artificial Intelligence

COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images

Parnian Afshar et al.

PATTERN RECOGNITION LETTERS (2020)

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Article Multidisciplinary Sciences

Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods

Mizuho Nishio et al.

SCIENTIFIC REPORTS (2020)

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Article Multidisciplinary Sciences

A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks

Tuan D. Pham

SCIENTIFIC REPORTS (2020)

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Article Environmental Sciences

Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data

Enzo Tartaglione et al.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2020)

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Article Biochemical Research Methods

Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks

Boran Sekeroglu et al.

SLAS TECHNOLOGY (2020)

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Article Engineering, Biomedical

Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning

Wanshan Ning et al.

NATURE BIOMEDICAL ENGINEERING (2020)

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Article Mathematics, Interdisciplinary Applications

Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches

Shayan Hassantabar et al.

CHAOS SOLITONS & FRACTALS (2020)

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Article Multidisciplinary Sciences

COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images

Linda Wang et al.

SCIENTIFIC REPORTS (2020)

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Article Multidisciplinary Sciences

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography

Jun Chen et al.

SCIENTIFIC REPORTS (2020)

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Article Mathematics, Interdisciplinary Applications

CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images

Chaimae Ouchicha et al.

CHAOS SOLITONS & FRACTALS (2020)

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Article Computer Science, Interdisciplinary Applications

CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images

Asif Iqbal Khan et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2020)

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Article Mathematics, Interdisciplinary Applications

Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique

Aytac Altan et al.

CHAOS SOLITONS & FRACTALS (2020)

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Article Biology

Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation

Amine Amyar et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2020)

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Article Computer Science, Interdisciplinary Applications

Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays

Luca Brunese et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2020)

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Article Engineering, Biomedical

COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings

Mohd Zulfaezal Che Azemin et al.

INTERNATIONAL JOURNAL OF BIOMEDICAL IMAGING (2020)

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Article Engineering, Biomedical

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IEEE ACCESS (2020)

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Article Computer Science, Artificial Intelligence

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2017)

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Article Multidisciplinary Sciences

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