COVID-19 Classification from X-rays : A Comparative Study

With the arrival of the most recent coronavirus pandemic, it was a must to find solutions to detect this dangerous virus. Analyzing X-ray images was among the exploited techniques to control this disease. However, the doctor's subjectivity in analyzing X-rays was the first obstacle in detecting this virus accurately. Applying new deep learning techniques to x-ray images can be a potential solution to reduce this subjectivity. This paper aims to conduct a comparative study between six different CNN architectures (VGG16, VGG19, Inception, Xception, DenseNet, and ChexNet) for COVID-19 detection from X-rays. The obtained results based on the transfer learning strategy confirm the efficiency of the VGG 16, where its achieved 98.69 % of accuracy on the COVID-19 Radiography Dataset. © 2022 IEEE.

COVID-19 Classification from X-rays : A Comparative Study

With the arrival of the most recent coronavirus pandemic, it was a must to find solutions to detect this dangerous virus. Analyzing X-ray images was among the exploited techniques to control this disease. However, the doctor's subjectivity in analyzing X-rays was the first obstacle in detecting this virus accurately. Applying new deep learning techniques to x-ray images can be a potential solution to reduce this subjectivity. This paper aims to conduct a comparative study between six different CNN architectures (VGG16, VGG19, Inception, Xception, DenseNet, and ChexNet) for COVID-19 detection from X-rays. The obtained results based on the transfer learning strategy confirm the efficiency of the VGG 16, where its achieved 98.69 % of accuracy on the COVID-19 Radiography Dataset. © 2022 IEEE.

Identification and Localization of COVID-19 Abnormalities on Chest Radiographs using Ensembled Deep Neural Networks

With the hit of the global pandemic COVID-19, the chest X-ray domain has gained prominence. It has been recognised as one of the principal methods to learn the presence of infection and its effect on various internal organs like the lungs. Chest radiographs show abnormalities due to COVID-19 that appear similar to the anomalies caused by other viruses and bacteria, thus making it challenging for technicians to detect. Therefore, it becomes almost inevitable to have a computer vision model that identifies and localizes the COVID-19 virus to help doctors provide an immediate and confident diagnosis. The models in computer vision tasks have seen considerable advancements in deep learning, so the proposed model tried to integrate a few of them to come up with a model for classifying and localising the diagnosis of COVID-19 using chest X-rays. This paper ensembles a few state-of-the-art models in classification and object detection to build a model for chest radiograph diagnosis. The proposed ensembled model is found to achieve the mean Average Precision value of 0.627 on SIIM-FISABIO-RSNA COVID-19 dataset. © 2022 IEEE.

CheXNet for the Evidence of Covid-19 Using 2.3K Positive Chest X-rays

CheXNet is not a surprise for Deep Learning (DL) community as it was primarily designed for radiologist-level pneumonia detection in Chest X-rays (CXRs). In this paper, we study CheXNet to analyze CXRs to detect the evidence of Covid-19. On a dataset of size 4, 600 CXRs (2, 300 Covid-19 positive cases and 2, 300 non-Covid cases (Healthy and Pneumonia cases)) and with k(=5) fold cross-validation technique, we achieve the following performance scores: accuracy of 0.98, AUC of 0.99, specificity of 0.98 and sensitivity of 0.99. On such a large dataset, our results can be compared with state-of-the-art results. © 2022, Springer Nature Switzerland AG.

VEntNet: Hybrid deep convolutional neural network model for automated multi-class categorization of chest X-rays

Visual interpretation of chest X-rays (CXRs) is tedious and prone to error. Significant amount of time is spent by the radiologist in differentiating normal from abnormal CXRs and in identifying the location and type of abnormalities. An assistance tool for automatically classifying normal and different types of abnormal CXRs can facilitate the diagnosis and potentially save time costs. In this paper, a novel hybrid model having concatenation of Visual Geometry Group (VGG19) network and Entropy features as a modified deep convolutional neural network (DCNN) architecture, called VEntNet, is proposed for the automated multi-class categorization of CXR images into normal, coronavirus disease (COVID), tuberculosis (TB), viral pneumonia, and bacterial pneumonia. The VEntNet model implemented consists of deep features extraction from convolutional layers of VGG19 network which are then concatenated with hand-crafted entropy features extracted from CXRs. The concatenated features are then fed to the fully connected (FC) layers for performing multi-class categorization using Softmax activation function. The performance of proposed VEntNet model is compared with other DCNNs with and without the hybrid approach for categorization of closely related lung pathologies and normal CXR images. Our proposed VEntNet achieved accuracies of 98.78% and 90.96%, respectively, for four and five-class classification of CXRs. Thus, it is demonstrated that among the different DCNNs, our VEntNet outperformed in four-class CXR categorization tasks. The proposed model can potentially save time by facilitating the screening of CXRs to identify those with abnormalities present as well as to categorize the abnormalities. © 2022 Wiley Periodicals LLC.

Machine Learning and Deep Learning Approaches to Analyze and Detect COVID-19: A Review.

COVID-19 also referred to as Corona Virus disease is a communicable disease that is caused by a coronavirus. Significant number of people who are tainted with this infection will have to brave and encounter moderate to severe respiratory sickness. Aged persons, sick, convalescing people and all those having underlying health complications like diabetes, chronic breathing diseases and cardiovascular diseases are bound to contract this sickness if not taken proper care of. At the current scenario, there are neither definite treatments nor inoculations against COVID-19. Nevertheless, there are numerous continuing clinical trials assessing the impending treatments and vaccines. Sensing the threatening impacts of Covid-19, researchers of computer science have started using various techniques and approaches of Machine Learning and Deep Learning to detect the presence of the disease using X-rays and CT images. The biggest stumbling block here is that there are only a few datasets available. There is also less number of experts for marking the information explicit to this new strain of infection in people. Artificial Intelligence centred tools can be designed and developed quickly for adapting the existing AI models and for leveraging the ability to modify and associating them with the preliminary clinical understanding to address the new group of COVID-19 and the novel challenges associated with it. In this paper, we look into a few techniques of Machine Learning and Deep Learning that have been employed to analyse Corona Virus Data.