An Approach for COVID-19 Identification from Chest X-ray Images Using High-Resolution Networks

In this paper, we present an approach for COVID-19 identification from chest X-ray images by using high-resolution neural networks. These networks allow to connect high-to-low convolution streams in parallel. They can maintain high-resolution representations and generate different resolutions throughout the whole process. The high-resolution based models have shown the superior performance in several applications. The experiments were evaluated on a collection of three data sources containing 24,786 lung X-ray images, which were categorized into three classes including covid pneumonia, non-pneumonia, and viral pneumonia. The proposed approach can attain the overall accuracy of 98.2% and 97.56% for the training and testing set, respectively. The accuracy for each class is 99.37%, 94.83%, and 97.27%, respectively, for non-pneumonia, covid-pneumonia, and viral-pneumonia. © 2022 IEEE.

Performance analysis of lightweight CNN models to segment infectious lung tissues of COVID-19 cases from tomographic images.

The pandemic of Coronavirus Disease-19 (COVID-19) has spread around the world, causing an existential health crisis. Automated detection of COVID-19 infections in the lungs from Computed Tomography (CT) images offers huge potential in tackling the problem of slow detection and augments the conventional diagnostic procedures. However, segmenting COVID-19 from CT Scans is problematic, due to high variations in the types of infections and low contrast between healthy and infected tissues. While segmenting Lung CT Scans for COVID-19, fast and accurate results are required and furthermore, due to the pandemic, most of the research community has opted for various cloud based servers such as Google Colab, etc. to develop their algorithms. High accuracy can be achieved using Deep Networks but the prediction time would vary as the resources are shared amongst many thus requiring the need to compare different lightweight segmentation model. To address this issue, we aim to analyze the segmentation of COVID-19 using four Convolutional Neural Networks (CNN). The images in our dataset are preprocessed where the motion artifacts are removed. The four networks are UNet, Segmentation Network (Seg Net), High-Resolution Network (HR Net) and VGG UNet. Trained on our dataset of more than 3,000 images, HR Net was found to be the best performing network achieving an accuracy of 96.24% and a Dice score of 0.9127. The analysis shows that lightweight CNN models perform better than other neural net models when to segment infectious tissue due to COVID-19 from CT slices.