CovMnet-Deep Learning Model for classifying Coronavirus (COVID-19).

Diagnosing COVID-19, current pandemic disease using Chest X-ray images is widely used to evaluate the lung disorders. As the spread of the disease is enormous many medical camps are being conducted to screen the patients and Chest X-ray is a simple imaging modality to detect presence of lung disorders. Manual lung disorder detection using Chest X-ray by radiologist is a tedious process and may lead to inter and intra-rate errors. Various deep convolution neural network techniques were tested for detecting COVID-19 abnormalities in lungs using Chest X-ray images. This paper proposes deep learning model to classify COVID-19 and normal chest X-ray images. Experiments are carried out for deep feature extraction, fine-tuning of convolutional neural networks (CNN) hyper parameters, and end-to-end training of four variants of the CNN model. The proposed CovMnet provide better classification accuracy of 97.4% for COVID-19 /normal than those reported in the previous studies. The proposed CovMnet model has potential to aid radiologist to monitor COVID-19 disease and proves to be an efficient non-invasive COVID-19 diagnostic tool for lung disorders.

Computer-aided diagnosis of COVID-19 from chest X-ray images using histogram-oriented gradient features and Random Forest classifier.

The decision-making process is very crucial in healthcare, which includes quick diagnostic methods to monitor and prevent the COVID-19 pandemic disease from spreading. Computed tomography (CT) is a diagnostic tool used by radiologists to treat COVID patients. COVID x-ray images have inherent texture variations and similarity to other diseases like pneumonia. Manually diagnosing COVID X-ray images is a tedious and challenging process. Extracting the discriminant features and fine-tuning the classifiers using low-resolution images with a limited COVID x-ray dataset is a major challenge in computer aided diagnosis. The present work addresses this issue by proposing and implementing Histogram Oriented Gradient (HOG) features trained with an optimized Random Forest (RF) classifier. The proposed HOG feature extraction method is evaluated with Gray-Level Co-Occurrence Matrix (GLCM) and Hu moments. Results confirm that HOG is found to reflect the local description of edges effectively and provide excellent structural features to discriminate COVID and non-COVID when compared to the other feature extraction techniques. The performance of the RF is compared with other classifiers such as Linear Regression (LR), Linear Discriminant Analysis (LDA), K-nearest neighbor (kNN), Classification and Regression Trees (CART), Random Forest (RF), Support Vector Machine (SVM), and Multi-layer perceptron neural network (MLP). Experimental results show that the highest classification accuracy (99. 73%) is achieved using HOG trained by using the Random Forest (RF) classifier. The proposed work has provided promising results to assist radiologists/physicians in automatic COVID diagnosis using X-ray images.

Diagnosis of covid-19 using optimized pca based local binary pattern features

Introduction: COVID-19 is a pandemic disease affecting the global mankind since December 2019. Diagnosing COVID-19 using lung X-ray image is a great challenge faced by the entire world. Early detection helps the doctors to suggest suitable treatment and also helps speedy recovery of the patients. Advancements in the field of computer vision aid medical practitioners to predict and diagnosis disease accurately. Objective: This study aims to analyze the chest X-ray for determining the presence of COVID-19 using machine learning algo-rithm. Methods: Researchers propose various techniques using machine learning algorithms and deep learning approaches to de-tect COVID-19. However, obtaining an accurate solution using these AI techniques is the main challenge still remains open to researchers. Results: This paper proposes a Local Binary Pattern technique to extract discriminant features for distinguishing COVID-19 disease using the X-ray images. The extracted features are given as input to various classifiers namely Random Forest (RF), Linear Discriminant Analysis (LDA), k-Nearest Neighbour (kNN), Classification and Regression Trees (CART), Support Vector Machine (SVM), Linear Regression (LR), and Multi-layer perceptron neural network (MLP). The proposed model has achieved an accuracy of 77.7% from Local Binary Pattern (LBP) features coupled with Random Forest classifier. Conclusion: The proposed algorithm analyzed COVID X-ray images to classify the data in to COVID-19 or not. The features are extracted and are classified using machine learning algorithms. The model achieved high accuracy for linear binary pattern with random forest classifier. © IJCRR.

Analysis of kubernetes for distributed healthcare system development using covid-19 healthcare app

Introduction: Distributed computing is a field of computer science which deals with the study of distributed systems A system which has communication and coordination with each of its nodes and which interacts with each other to achieve a common target which is to effectively compute the computation. These capabilities are conducive to implementing a systematic and efficient COVID-19 tracking application which can be accessed and worked on by numerous entities. Objective: To provide information about a client-server architecture which is a platform for managing and maintaining container-ized workloads and services that forms a base for automation. Methods: A Kubernetes cluster IS created with a calico pod network along with the main drivers of Kubelet, Kubeadm and Kubectl. Secure Shell (SSH) protocol is used for secured shell and data management and authentication between the client and server. Results: We have performed and distributed our tasks in such a way to show the developers that multiple tasks can be performed at the same time using Kubernetes orchestration platform and used to parallelize multiple tasks. This increases the efficiency of the machine and the performance of the system becomes much faster. Conclusion: A system which has communication and coordination with each of its nodes and which interacts with each other to achieve a common target which is to effectively compute the computation. One such application which helps in the distribution of tasks and helps do the computation is Kubernetes. It is based on a client-server architecture which is a platform for managing and maintaining containerized workloads and services that forms a base for automation. © IJCRR.