Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A review

Abstract

Background: This paper provides a systematic review of the application of Artificial Intelligence (AI) in the form of Machine Learning (ML) and Deep Learning (DL) techniques in fighting against the effects of novel coronavirus disease (COVID-19). Objective & Methods: The objective is to perform a scoping review on AI for COVID-19 using preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. A literature search was performed for relevant studies published from 1 January 2020 till 27 March 2021. Out of 4050 research papers available in reputed publishers, a full-text review of 440 articles was done based on the keywords of AI, COVID-19, ML, forecasting, DL, X-ray, and Computed Tomography (CT). Finally, 52 articles were included in the result synthesis of this paper. As part of the review, different ML regression methods were reviewed first in predicting the number of confirmed and death cases. Secondly, a comprehensive survey was carried out on the use of ML in classifying COVID-19 patients. Thirdly, different datasets on medical imaging were compared in terms of the number of images, number of positive samples and number of classes in the datasets. The different stages of the diagnosis, including preprocessing, segmentation and feature extraction were also reviewed. Fourthly, the performance results of different research papers were compared to evaluate the effectiveness of DL methods on different datasets. Results: Results show that residual neural network (ResNet-18) and densely connected convolutional network (DenseNet 169) exhibit excellent classification accuracy for X-ray images, while DenseNet-201 has the maximum accuracy in classifying CT scan images. This indicates that ML and DL are useful tools in assisting researchers and medical professionals in predicting, screening and detecting COVID-19.

17 Pages, 3 Figures

References in corpus (19)

• 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
• CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection
• COVID-CT-Dataset: A CT Scan Dataset about COVID-19
• CORD-19: The COVID-19 Open Research Dataset
• Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil
• Hybrid Deep Learning for Detecting Lung Diseases from X-ray Images
• Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts
• 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
• Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation
• Large-Scale Screening of COVID-19 from Community Acquired Pneumonia using Infection Size-Aware Classification
• Diagnosis of Breast Cancer Based on Modern Mammography using Hybrid Transfer Learning
• POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)
• Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation
• Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review
• A time series method to analyze incidence pattern and estimate reproduction number of COVID-19
• Short-term forecasts of COVID-19 spread across Indian states until 1 May 2020