Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning

[1]  Zohair Ahmed,et al.  An efficient deep learning-based framework for tuberculosis detection using chest X-ray images. , 2022, Tuberculosis.

[2]  Phuong T. Nguyen,et al.  Detection of tuberculosis from chest X-ray images: Boosting the performance with vision transformer and transfer learning , 2021, Expert Syst. Appl..

[3]  C. Bhatt,et al.  Detection and analysis of COVID-19 in medical images using deep learning techniques , 2021, Scientific Reports.

[4]  Marwa S. Elpeltagy,et al.  Automatic prediction of COVID− 19 from chest images using modified ResNet50 , 2021, Multimedia Tools and Applications.

[5]  Raymond Y Huang,et al.  Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT , 2021, Radiology.

[6]  A. Saygılı.  A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods , 2021, Applied Soft Computing.

[7]  Michele Flammini,et al.  Unavailable Transit Feed Specification: Making It Available With Recurrent Neural Networks , 2021, IEEE Transactions on Intelligent Transportation Systems.

[8]  Ali Narin,et al.  Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks , 2020, Pattern Analysis and Applications.

[9]  Mohammad Rahimzadeh,et al.  A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset , 2021, Biomedical Signal Processing and Control.

[10]  Alexander Wong,et al.  COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images , 2020, Frontiers in Medicine.

[11]  Isabel de la Torre Díez,et al.  Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network , 2020, Applied Soft Computing.

[12]  M. Flammini,et al.  Deep Learning for Automated Recognition of Covid-19 from Chest X-ray Images , 2020, medRxiv.

[13]  X. He,et al.  Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans , 2020, medRxiv.

[14]  T. Anwar,et al.  Deep learning based diagnosis of COVID-19 using chest CT-scan images , 2020, 2020 IEEE 23rd International Multitopic Conference (INMIC).

[15]  Z. Fayad,et al.  Artificial intelligence–enabled rapid diagnosis of patients with COVID-19 , 2020, Nature Medicine.

[16]  W. Liang,et al.  Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography , 2020, Cell.

[17]  U. Rajendra Acharya,et al.  Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks , 2020, Computers in Biology and Medicine.

[18]  Plamen Angelov,et al.  SARS-CoV-2 CT-scan dataset:A large dataset of real patients CT scans for SARS-CoV-2 identification , 2020 .

[19]  U. Rajendra Acharya,et al.  Automated detection of COVID-19 cases using deep neural networks with X-ray images , 2020, Computers in Biology and Medicine.

[20]  J. Connors,et al.  COVID-19 and its implications for thrombosis and anticoagulation , 2020, Blood.

[21]  Raymond Y Huang,et al.  AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT , 2020, Radiology.

[22]  Hien Van Nguyen,et al.  Radiologist-Level COVID-19 Detection Using CT Scans with Detail-Oriented Capsule Networks , 2020, ArXiv.

[23]  Eduardo José da S. Luz,et al.  Towards an Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images , 2020 .

[24]  Farnoosh Naderkhani,et al.  COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images , 2020, Pattern Recognition Letters.

[25]  Lawrence O. Hall,et al.  Finding Covid-19 from Chest X-rays using Deep Learning on a Small Dataset , 2020, ArXiv.

[26]  Davide Di Ruscio,et al.  Automated fruit recognition using EfficientNet and MixNet , 2020, Comput. Electron. Agric..

[27]  Bonggun Shin,et al.  Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model , 2020, Computational and Structural Biotechnology Journal.

[28]  Chunhua Shen,et al.  COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection , 2020, ArXiv.

[29]  Mohamed Medhat Gaber,et al.  Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network , 2020, Applied Intelligence.

[30]  Ioannis D. Apostolopoulos,et al.  Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks , 2020, Physical and Engineering Sciences in Medicine.

[31]  Joseph Paul Cohen,et al.  COVID-19 Image Data Collection , 2020, ArXiv.

[32]  Ezz El-Din Hemdan,et al.  COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images , 2020, ArXiv.

[33]  Allan Tucker,et al.  Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection , 2020, ArXiv.

[34]  A. Wong,et al.  COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images , 2020, Scientific Reports.

[35]  K. Cao,et al.  Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT , 2020, Radiology.

[36]  Yi Yang,et al.  Lower mortality of COVID-19 by early recognition and intervention: experience from Jiangsu Province , 2020, Annals of Intensive Care.

[37]  Li Yan,et al.  Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan , 2020 .

[38]  Xian-gao Jiang,et al.  Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity , 2020 .

[39]  Yanjie Wei,et al.  Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov , 2020, Interdisciplinary Sciences: Computational Life Sciences.

[40]  Quoc V. Le,et al.  Adversarial Examples Improve Image Recognition , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[41]  Quoc V. Le,et al.  Self-Training With Noisy Student Improves ImageNet Classification , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[42]  Alexander Wong,et al.  COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images , 2020, ArXiv.

[43]  Quoc V. Le,et al.  MixConv: Mixed Depthwise Convolutional Kernels , 2019, BMVC.

[44]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[45]  Song Han,et al.  ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware , 2018, ICLR.

[46]  Bo Chen,et al.  MnasNet: Platform-Aware Neural Architecture Search for Mobile , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[47]  Andreas Kamilaris,et al.  Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..

[48]  Andrew G. Howard,et al.  Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation , 2018, ArXiv.

[49]  Paulo S. C. Alencar,et al.  The use of machine learning algorithms in recommender systems: A systematic review , 2015, Expert Syst. Appl..

[50]  Zongxu Pan,et al.  Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data , 2017, Remote. Sens..

[51]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[52]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[53]  Taghi M. Khoshgoftaar,et al.  A survey of transfer learning , 2016, Journal of Big Data.

[54]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[55]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[56]  Jude W. Shavlik,et al.  Using Advice to Transfer Knowledge Acquired in One Reinforcement Learning Task to Another , 2005, ECML.

[57]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .