A Review of Automated Diagnosis of COVID-19 Based on Scanning Images

The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically. Due to the false-negative rate and the time-consuming of the conventional Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, diagnosing based on X-ray images and Computed Tomography (CT) images has been widely adopted. Therefore, researchers of the computer vision area have developed many automatic diagnosing models based on machine learning or deep learning to assist the radiologists and improve the diagnosing accuracy. In this paper, we present a review of these recently emerging automatic diagnosing models. 69 models proposed from February 14, 2020, to July 21, 2020, are involved. We analyzed the models from the perspective of preprocessing, feature extraction, classification, and evaluation. Based on the limitation of existing models, we pointed out that domain adaption in transfer learning and interpretability promotion would be the possible future directions.

[1]  Sameer K. Antani,et al.  Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays , 2020, IEEE Access.

[2]  Yuan Gao,et al.  Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images , 2020, IEEE Access.

[3]  D.-P. Fan,et al.  Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images , 2020, IEEE Transactions on Medical Imaging.

[4]  Dalia Ezzat,et al.  GSA-DenseNet121-COVID-19: a Hybrid Deep Learning Architecture for the Diagnosis of COVID-19 Disease based on Gravitational Search Optimization Algorithm , 2020, ArXiv.

[5]  Miguel Cazorla,et al.  UMLS-ChestNet: A deep convolutional neural network for radiological findings, differential diagnoses and localizations of COVID-19 in chest x-rays , 2020, ArXiv.

[6]  Yaozong Gao,et al.  Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia , 2020, IEEE Transactions on Medical Imaging.

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

[8]  Avik Santra,et al.  COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-19 , 2020, ArXiv.

[9]  V. Kamalaveni,et al.  Image Denoising Using Variations of Perona-Malik Model with Different Edge Stopping Functions☆ , 2015 .

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

[11]  Z. Hasan A Survey on Shari’Ah Governance Practices in Malaysia, GCC Countries and the UK , 2011 .

[12]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Ming-Ming Cheng,et al.  JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation , 2020, IEEE Transactions on Image Processing.

[14]  Hayit Greenspan,et al.  Coronavirus Detection and Analysis on Chest CT with Deep Learning , 2020, ArXiv.

[15]  Dongxiao Zhu,et al.  COVID-MobileXpert: On-Device COVID-19 Screening using Snapshots of Chest X-Ray , 2020, ArXiv.

[16]  Saptarshi Purkayastha,et al.  Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications , 2020, ArXiv.

[17]  Haibo Xu,et al.  AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks , 2020, medRxiv.

[18]  Morteza Heidari,et al.  Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms , 2020, International Journal of Medical Informatics.

[19]  Yaozong Gao,et al.  Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT , 2020, IEEE Journal of Biomedical and Health Informatics.

[20]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[21]  Sushmita Mitra,et al.  Deep Learning for Screening COVID-19 using Chest X-Ray Images , 2020, 2020 IEEE Symposium Series on Computational Intelligence (SSCI).

[22]  Nour Eldeen M. Khalifa,et al.  Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset , 2020, AISI.

[23]  Kayhan Zrar Ghafoor,et al.  Diagnosing COVID-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms , 2020, Defense + Commercial Sensing.

[24]  Mamun Bin Ibne Reaz,et al.  Can AI Help in Screening Viral and COVID-19 Pneumonia? , 2020, IEEE Access.

[25]  Kadry Ali Ezzat,et al.  Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector Machine , 2020, medRxiv.

[26]  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.

[27]  Tie-Yan Liu,et al.  LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.

[28]  Philip S. Yu,et al.  A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 , 2020, ACM Comput. Surv..

[29]  Kevin A. Schneider,et al.  Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach , 2020, Biocybernetics and Biomedical Engineering.

[30]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[31]  Yi Li,et al.  Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection , 2020 .

[32]  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.

[33]  Daniel Mossé,et al.  V-NET: a framework for a versatile network architecture to support real-time communication performance guarantees , 1995, Proceedings of INFOCOM'95.

[34]  Dinggang Shen,et al.  Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 , 2020, IEEE Reviews in Biomedical Engineering.

[35]  Mahesh Gour,et al.  Stacked Convolutional Neural Network for Diagnosis of COVID-19 Disease from X-ray Images , 2020, ArXiv.

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

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

[38]  Md. Kamrul Hasan,et al.  CVR-Net: A deep convolutional neural network for coronavirus recognition from chest radiography images , 2020, ArXiv.

[39]  Milan Sonka,et al.  Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning , 2020, Medical Image Analysis.

[40]  Alexander 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.

[41]  Jieli Zhou,et al.  SODA: Detecting COVID-19 in Chest X-Rays With Semi-Supervised Open Set Domain Adaptation , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[42]  Ji Feng,et al.  Deep Forest: Towards An Alternative to Deep Neural Networks , 2017, IJCAI.

[43]  Yaozong Gao,et al.  Large-Scale Screening of COVID-19 from Community Acquired Pneumonia using Infection Size-Aware Classification , 2020, ArXiv.

[44]  Vijayan K. Asari,et al.  COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches , 2020, ArXiv.

[45]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[46]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[47]  Dijia Wu,et al.  Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning , 2020, IEEE Transactions on Medical Imaging.

[48]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[49]  Yan Bai,et al.  A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis , 2020, European Respiratory Journal.

[50]  Mizuho Nishio,et al.  Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods , 2020, Scientific Reports.

[51]  K. Cao,et al.  Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy , 2020 .

[52]  Richard D Riley,et al.  Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal , 2020, BMJ.

[53]  Petia Radeva,et al.  Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images , 2020, ArXiv.

[54]  Arash Maghsoudi,et al.  A Novel and Reliable Deep Learning Web-Based Tool to Detect COVID-19 Infection from Chest CT-Scan , 2020, ArXiv.

[55]  Lina Yao,et al.  Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images , 2020, Pattern Recognition.

[56]  Yifan Zhang,et al.  COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19 , 2020, ArXiv.

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

[58]  Manoranjan Paul,et al.  Computer Vision for COVID-19 Control: A Survey , 2020, ArXiv.

[59]  Su Ruan,et al.  An automatic COVID-19 CT segmentation based on U-Net with attention mechanism , 2020 .

[60]  Wenyu Liu,et al.  Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label , 2020, medRxiv.

[61]  Yaozong Gao,et al.  Lung Infection Quantification of COVID-19 in CT Images with Deep Learning , 2020, ArXiv.

[62]  Yuanjie Zheng,et al.  Robust Screening of COVID-19 from Chest X-ray via Discriminative Cost-Sensitive Learning , 2020, ArXiv.

[63]  Umut Ozkaya,et al.  Coronavirus (COVID-19) Classification Using Deep Features Fusion and Ranking Technique , 2020, Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach.

[64]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[66]  Alexandra Luccioni,et al.  Mapping the Landscape of Artificial Intelligence Applications against COVID-19 , 2020, J. Artif. Intell. Res..

[67]  Lina Yao,et al.  Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images , 2020, ArXiv.

[68]  Aniruddha Pant,et al.  Automated Detection of COVID-19 from CT Scans Using Convolutional Neural Networks , 2020, ICPRAM.

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

[70]  Ioannis D. Apostolopoulos,et al.  Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases , 2020, Journal of medical and biological engineering.

[71]  Junwei Su,et al.  Deep learning system to screen coronavirus disease 2019 pneumonia , 2020 .

[72]  Jing Xu,et al.  MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation , 2020, AAAI.

[73]  Nai-Kuan Chou,et al.  A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening , 2020, ArXiv.

[74]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[75]  Sonali Agarwal,et al.  Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks , 2020, Appl. Intell..

[76]  Abhishek Das,et al.  Grad-CAM: Why did you say that? , 2016, ArXiv.

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

[78]  Till Döhmen,et al.  DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images , 2020 .

[79]  Hayit Greenspan,et al.  Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis , 2020, ArXiv.

[80]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[81]  Linkai Luo,et al.  Deep Learning-Based Recognizing COVID-19 and other Common Infectious Diseases of the Lung by Chest CT Scan Images , 2020, medRxiv.

[82]  Jong Chul Ye,et al.  Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets , 2020, IEEE Transactions on Medical Imaging.

[83]  Chulhong Kim,et al.  Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19 , 2020, Electronics.

[84]  Bo Xu,et al.  A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19) , 2020, European Radiology.

[85]  Yi Zhong,et al.  Using Deep Convolutional Neural Networks to Diagnose COVID-19 From Chest X-Ray Images , 2020, ArXiv.

[86]  Brian D Goodwin,et al.  Intra-model Variability in COVID-19 Classification Using Chest X-ray Images , 2020, ArXiv.

[87]  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.

[88]  Geoffrey E. Hinton,et al.  Dynamic Routing Between Capsules , 2017, NIPS.

[89]  Thomas Brox,et al.  3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.

[90]  Su Ruan,et al.  An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism , 2020, ArXiv.

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

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

[93]  Eduardo José da S. Luz,et al.  Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images , 2020, Research on Biomedical Engineering.

[94]  Fatemeh Homayounieh,et al.  CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image , 2020, ArXiv.

[95]  Yuedong Yang,et al.  Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[96]  Lian-lian Wu,et al.  Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study , 2020, medRxiv.

[97]  U. Rajendra Acharya,et al.  Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review , 2020, ArXiv.

[98]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[99]  Amine Naït-Ali,et al.  Detection of Covid-19 From Chest X-ray Images Using Artificial Intelligence: An Early Review , 2020, ArXiv.

[100]  Yandre M. G. Costa,et al.  COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios , 2020, Computer Methods and Programs in Biomedicine.

[101]  Mei Wang,et al.  Deep Visual Domain Adaptation: A Survey , 2018, Neurocomputing.

[102]  Asif Iqbal Khan,et al.  CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images , 2020, Computer Methods and Programs in Biomedicine.

[103]  Thanh Thi Nguyen,et al.  Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions , 2020, ArXiv.

[104]  Jie Zhou,et al.  Development and Evaluation of an AI System for COVID-19 , 2020, medRxiv.

[105]  Serkan Kiranyaz,et al.  Convolutional Sparse Support Estimator-Based COVID-19 Recognition From X-Ray Images , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[106]  Yunxiang Li,et al.  A cascade network for Detecting COVID-19 using chest x-rays , 2020, ArXiv.

[107]  Abdul Hafeez,et al.  COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs , 2020, ArXiv.

[108]  Mohammad Rahimzadeh,et al.  A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2 , 2020, Informatics in Medicine Unlocked.

[109]  Luigi Cinque,et al.  A light CNN for detecting COVID-19 from CT scans of the chest , 2020, Pattern Recognition Letters.