相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Deep Transfer Learning Based Classification Model for COVID-19 Disease
Y. Pathak et al.
IRBM (2022)
Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning
Aayush Jaiswal et al.
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS (2021)
A deep learning system that generates quantitative CT reports for diagnosing pulmonary Tuberculosis
Xukun Li et al.
APPLIED INTELLIGENCE (2021)
A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)
Shuai Wang et al.
EUROPEAN RADIOLOGY (2021)
Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review
Mustafa Ghaderzadeh et al.
JOURNAL OF HEALTHCARE ENGINEERING (2021)
Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China
Dawei Wang et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2020)
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Chaolin Huang et al.
LANCET (2020)
Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia
Qun Li et al.
NEW ENGLAND JOURNAL OF MEDICINE (2020)
Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases
Tao Ai et al.
RADIOLOGY (2020)
CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV)
Michael Chung et al.
RADIOLOGY (2020)
The Extent of Transmission of Novel Coronavirus in Wuhan, China, 2020
Hiroshi Nishiura et al.
JOURNAL OF CLINICAL MEDICINE (2020)
Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography
Kang Zhang et al.
CELL (2020)
Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet
Harsh Panwar et al.
CHAOS SOLITONS & FRACTALS (2020)
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)
Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review
Zheng Ye et al.
EUROPEAN RADIOLOGY (2020)
A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis
Shuo Wang et al.
EUROPEAN RESPIRATORY JOURNAL (2020)
Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy
Lin Li et al.
RADIOLOGY (2020)
Artificial intelligence-enabled rapid diagnosis of patients with COVID-19
Xueyan Mei et al.
NATURE MEDICINE (2020)
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)
End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT
Jiangdian Song et al.
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2020)
A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images
Qianqian Ni et al.
EUROPEAN RADIOLOGY (2020)
Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning
Shervin Minaee et al.
MEDICAL IMAGE ANALYSIS (2020)
Contrastive Cross-Site Learning With Redesigned Net for COVID-19 CT Classification
Zhao Wang et al.
IEEE Journal of Biomedical and Health Informatics (2020)
Extension of Coronavirus Disease 2019 on Chest CT and Implications for Chest Radiographic Interpretation(vol 2, artn e200107, year 2020)
Hyewon Choi et al.
RADIOLOGY-CARDIOTHORACIC IMAGING (2020)
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)
A survey on Image Data Augmentation for Deep Learning
Connor Shorten et al.
JOURNAL OF BIG DATA (2019)
Convolutional neural networks: an overview and application in radiology
Rikiya Yamashita et al.
INSIGHTS INTO IMAGING (2018)
A survey on deep learning in medical image analysis
Geert Litjens et al.
MEDICAL IMAGE ANALYSIS (2017)