Background and aims: Advances in
artificial intelligence and rapid changes in medical equipmenthave provided many opportunities in medical radiology. Advanced algorithms based ondeep learning, known as Convolutional Neural Network, play a significant role in extracting basicfeatures in medical images. The aim of this study was to investigate and compare machine learningand
deep learning algorithms used to solve a clinical problem in medical images in order toimprove the diagnosis of Covid-۱۹ and increase the speed and accuracy in diagnosing this disease.Method: In this study, articles that used
machine learning and
deep learning algorithms to solvea clinical problem in medical images were reviewed. To identify related articles, PubMed, Web ofScience, Scopus and Google scholar search engines were searched based on keywords. Differentalgorithms based on
artificial intelligence were extracted in the situation of the spread of the coronavirus. Automated techniques for classifying chest X-rays into pneumonia class or disease-freeclass are used from ۹
deep learning architectures including basic CNN, DenseNet۲۰۱, VGG۱۶,VGG۱۹, Inception_ResNet_V۲, Inception_V۳, Xception, Resnet۵۰. Finally, the type of algorithmused and its accuracy percentage were examined and then the algorithms were compared.Results: Overall, ۱۳ studies had used
deep learning algorithms on COVID-۱۹ and healthy individuals’data. The most advanced COVID-۱۹ diagnostic systems that were used to identify andclassify COVID-۱۹ patients and normal individuals based on accuracy were: ResNet۵۰ (۹۶.۰۱)ResNet۱۰۱ (۹۶.۰۱) ResNet۱۵۲ (۹۳.۰۹) Inception network from V۱ to V۴ (۹۸.۷۰ and ۹۷.۹۷%) andCNN (۹۷.۶۲ and ۹۹.۴%). Therefore, the review of studies shows that
deep learning with ConvolutionalNeural Networks (CNN) can have significant effects in automatic detection and extractionof very essential features from chest images related to the diagnosis of the corona virus. Advancedalgorithms based on deep learning, known as Convolutional Neural Network, play a significantrole in extracting basic features in high-precision medical images, and this method has been appliedusing CT and X-Ray image scans with significant results.Conclusion: Advanced algorithms based on deep learning, known as Convolutional Neural Networks,play a significant role in extracting essential features in high-precision medical images,and deep learning-based diagnosis systems can be useful in areas where experts and wellequippedclinics are not available.