Medical Imaging and Computational Image Analysis in COVID-19 Diagnosis: A Review

Abstract

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. Sometimes the symptoms of the disease increase so much they lead to the death of the patients. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. Many studies have tried to use medical imaging for early diagnosis of COVID-19. This study attempts to review papers on automatic methods for medical image analysis and diagnosis of COVID-19. For this purpose, PubMed, Google Scholar, arXiv and medRxiv were searched to find related studies by the end of April 2020, and the essential points of the collected studies were summarised. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis based on the accuracy and the method used, 4) to express the research limitations in this field and the methods used to overcome them. COVID-19 reveals signs in medical images can be used for early diagnosis of the disease even in asymptomatic patients. Using automated machine learning-based methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.

29 pages, 4 tables

References in corpus (25)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks
• Can AI help in screening Viral and COVID-19 pneumonia?
• CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
• COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images
• COVID-19 Image Data Collection: Prospective Predictions Are the Future
• COVID-19 Image Data Collection
• COVID-CT-Dataset: A CT Scan Dataset about COVID-19
• COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios
• COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs
• JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation
• Extracting possibly representative COVID-19 Biomarkers from X-Ray images with Deep Learning approach and image data related to Pulmonary Diseases
• Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection
• Coronavirus (COVID-19) Classification using CT Images by Machine Learning Methods
• Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
• GSA-DenseNet121-COVID-19: a Hybrid Deep Learning Architecture for the Diagnosis of COVID-19 Disease based on Gravitational Search Optimization Algorithm
• Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images
• Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images
• Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
• Coronavirus Detection and Analysis on Chest CT with Deep Learning
• Radiologist-Level COVID-19 Detection Using CT Scans with Detail-Oriented Capsule Networks
• DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images
• Detection of Covid-19 From Chest X-ray Images Using Artificial Intelligence: An Early Review
• COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays

Cited by in corpus (4)

• Self-supervised deep convolutional neural network for chest X-ray classification
• Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels
• An Explainable Deep Learning-Based Method For Schizophrenia Diagnosis Using Generative Data-Augmentation
• Prognostic Model for Idiopathic Pulmonary Fibrosis Using Context-Aware Sequential-Parallel Hybrid Transformer and Enriched Clinical Information