A systematic review: Chest radiography images (X-ray images) analysis and COVID-19 categorization diagnosis using artificial intelligence techniques.

COVID-19 pandemic created a turmoil across nations due to Severe Acute Respiratory Syndrome Corona virus-1(SARS - Co-V-2). The severity of COVID-19 symptoms is starting from cold, breathing problems, issues in respiratory system which may also lead to life threatening situations. This disease is widely contaminating and transmitted from man-to-man. The contamination is spreading when the human organs like eyes, nose, and mouth get in contact with contaminated fluids. This virus can be screened through performing a nasopharyngeal swab test which is time consuming. So the physicians are preferring the fast detection methods like chest radiography images and CT scans. At times some confusion in finding out the accurate disorder from chest radiography images can happen. To overcome this issue this study reviews several deep learning and machine learning procedures to be implemented in X-ray images of chest. This also helps the professionals to find out the other types of malfunctions happening in the chest other than COVID-19 also. This review can act as a guidance to the doctors and radiologists in identifying the COVID-19 and other types of viruses causing illness in the human anatomy and can provide aid soon.

Lightweight ResGRU: a deep learning-based prediction of SARS-CoV-2 (COVID-19) and its severity classification using multimodal chest radiography images.

The new COVID-19 emerged in a town in China named Wuhan in December 2019, and since then, this deadly virus has infected 324 million people worldwide and caused 5.53 million deaths by January 2022. Because of the rapid spread of this pandemic, different countries are facing the problem of a shortage of resources, such as medical test kits and ventilators, as the number of cases increased uncontrollably. Therefore, developing a readily available, low-priced, and automated approach for COVID-19 identification is the need of the hour. The proposed study uses chest radiography images (CRIs) such as X-rays and computed tomography (CTs) to detect chest infections, as these modalities contain important information about chest infections. This research introduces a novel hybrid deep learning model named Lightweight ResGRU that uses residual blocks and a bidirectional gated recurrent unit to diagnose non-COVID and COVID-19 infections using pre-processed CRIs. Lightweight ResGRU is used for multi-modal two-class classification (normal and COVID-19), three-class classification (normal, COVID-19, and viral pneumonia), four-class classification (normal, COVID-19, viral pneumonia, and bacterial pneumonia), and COVID-19 severity types' classification (i.e., atypical appearance, indeterminate appearance, typical appearance, and negative for pneumonia). The proposed architecture achieved f-measure of 99.0%, 98.4%, 91.0%, and 80.5% for two-class, three-class, four-class, and COVID-19 severity level classifications, respectively, on unseen data. A large dataset is created by combining and changing different publicly available datasets. The results prove that radiologists can adopt this method to screen chest infections where test kits are limited.

Convolutional Neural Network (CNN) in COVID-19 Detection: A Case Study with Chest CT Scan Images

Deep Learning, especially Convolutional Neural Net-works (CNN) have been performing very well for the last decade in medical image classification. CNN has already shown a great prospect in detecting COVID-19 from chest X-ray images. However, due to its three dimensional data, chest CT scan images can provide better understanding of the affected area through segmentation in comparison to the chest X-ray images. But the chest CT scan images have not been explored enough to achieve sufficiently good results in comparison to the X-ray images. However, with proper image pre-processing, fine tuning, and optimization of the models better results can be achieved. This work aims in contributing to filling this void in the literature. On this aspect, this work explores and designs both custom CNN model and three other models based on transfer learning: InceptionV3, ResNet50, and VGG19. The best performing model is VGG19 with an accuracy of 98.39% and F-1 score of 98.52%. The main contribution of this work includes: (i) modeling a custom CNN model and three pre-trained models based on InceptionV3, ResNet50, and VGG19 (ii) training and validating the models with a comparatively larger dataset of 1252 COVID-19 and 1230 non-COVID CT images (iii) fine tune and optimize the designed models based on the parameters like number of dense layers, optimizer, learning rate, batch size, decay rate, and activation functions to achieve better results than the most of the state-of-the-art literature (iv) the designed models are made public in [1] for reproducibility by the research community for further developments and improvements. © 2022 IEEE.