Throughout history, epidemics and chronic diseases have claimed the lives of many people and caused major crises that have taken a long time to overcome. The 2019 novel coronavirus (COVID-19) pandemic appeared in Wuhan, China in December 2019 and has become a serious public health problem worldwide. It is an acute resolved disease, but it can also be deadly, with a 2% case fatality rate. The early and automatic diagnosis of Covid-19 may be beneficial for timely referral of the patient to quarantine, and monitoring of the spread of the disease. Some tests requiring significant time to produce results (days), and a projected up to 30% false positive rate, other timely approaches to diagnosis are worthy of investigation. Gold standard testing currently is a RNA-based assay using nasopharyngeal swabs. Identifying positive COVID-19 cases in early stages helps with isolating the patients as quickly as possible, hence breaking the chain of transition and ﬂattening the epidemic curve.
Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as MERS, COVID-19. Recently, researchers, specialists, and companies around the world are rolling out deep learning and image processing-based systems that can fastly process hundreds of X-Ray and computed tomography (CT) images to accelerate the diagnosis of pneumonia such as SARS, COVID-19, and aid in its containment. The false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly.
Consequently, there has been an urgent surge of interest to develop Deep Neural Network (DNN)-based diagnosis solutions, mainly based on Convolutional Neural Networks (CNNs), to facilitate identiﬁcation of positive COVID-19 cases. CNNs, however, are prone to lose spatial information between image instances and require large datasets.
In work Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks, a dataset of X-Ray images from patients with common pneumonia, Covid-19, and normal incidents was utilized for the automatic detection of the Coronavirus. The aim of the study is to evaluate the performance of Convolutional Neural Network architectures proposed over recent years for medical image classification. With transfer learning, an overall accuracy of 97.82% in the detection of Covid-19 is achieved.
In paper Automated Methods for Detection and Classification Pneumonia based on X-Ray Images Using Deep Learning, authors present a comparison of recent Deep Convolutional Neural Network (DCNN) architectures for automatic binary classification of pneumonia images based fined tuned versions of (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, MobileNet_V2 and Xception). As result they can conclude that fine-tuned version of Resnet50, MobileNet_V2 and Inception_Resnet_V2 show highly satisfactory performance with rate of increase in training and validation accuracy (more than 96% of accuracy).
In work Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep Learning and Transfer Learning Algorithms, authors focus on proposing AI tools that can be used by radiologists or healthcare professionals to diagnose COVID-19 cases in a quick and accurate manner. This study aims to build a comprehensive dataset of X-rays and CT scan images from multiple sources as well as provides a simple but an effective COVID-19 detection technique using deep learning and transfer learning algorithms. A simple convolution neural network (CNN) and modified pre- trained AlexNet model are applied on the prepared X-rays and CT scan images dataset. The result of the experiments shows that the utilized models can provide accuracy up to 98% via pre-trained network and 94.1% accuracy by using the modified CNN.
FINDING COVID-19 FROM CHEST X-RAYS USING DEEP LEARNING ON A SMALL DATASET explores how useful chest X-ray images can be in diagnosing COVID-19 disease. A pre- trained deep convolutional neural network has been tuned on 102 COVID-19 cases and 102 other pneumonia cases in a 10-fold cross validation. On a test set of 20 unseen COVID-19 cases all were correctly classified and more than 95% of 4,171 other pneumonia examples were correctly classified. COVID-CAPS: A CAPSULE NETWORK-BASED FRAMEWORK FOR IDENTIFICATION OF COVID-19 CASES FROM X-RAY IMAGES presents an alternative modeling framework based on Capsule Networks, referred to as the COVID-CAPS, being capable of handling small datasets, which is of signiﬁcant importance due to sudden and rapid emergence of COVID-19. Initial results based on a dataset of X-ray images show that COVID-CAPS has advantage over previous CNN-based models.