Utilization of Transfer Learning Model in Detecting COVID-19 Cases From Chest X-Ray Images

Diagnosis of COVID-19 pneumonia using patients' chest x-ray images is new but yet important task in the field of medicine. Researchers from different parts of the globe have developed many deep learning models to classify COVID-19. The performance of feature extraction and classifier plays a vital role in the recognizing the different patterns in the image. The pivotal process is the extraction of optimum features from the chest x-ray images. The main goal of this study is to design an efficient hybrid algorithm that integrates the robustness of MobileNet (using transfer learning approach) to extract features and support vector machine (SVM) to classify COVID-19. Experiments were conducted to test the proposed algorithm, and it was found to have a high classification accuracy of 95%.

A Survey of COVID-19 Detection From Chest X-Rays Using Deep Learning Methods

The coronavirus (COVID-19) outbreak has opened an alarming situation for the whole world and has been marked as one of the most severe and acute medical conditions in the last hundred years. Various medical imaging modalities including computer tomography (CT) and chest x-rays are employed for diagnosis. This paper presents an overview of the recently developed COVID-19 detection systems from chest x-ray images using deep learning approaches. This review explores and analyses the data sets, feature engineering techniques, image pre-processing methods, and experimental results of various works carried out in the literature. It also highlights the transfer learning techniques and different performance metrics used by researchers in this field. This information is helpful to point out the future research direction in the domain of automatic diagnosis of COVID-19 using deep learning techniques.

Exploring the Impact of Indian Revenues During COVID-19 Using Social Network Analysis

The second wave of the COVID-19 pandemic affected economy resulting in job loss for many throughout the world. The main objective is to analyze the Indian revenues before COVID-19, i.e., before December 2019 and the ongoing COVID-19 pandemic. A comparison is done between the revenues that are collected from various departments sectors for the years 2019–2020 and 2020–2021 to find out how the COVID-19 has affected the Indian economy as well as jobs for millions of people. Studying the network through social network analysis helps us to understand clearly how the Indian economy has changed during the COVID-19 period. The network is studied using an average path along with a weighted path to ascertain the small word characteristics. Centrality measures helps us to understand the characteristics of the dataset. The tools used to analyze them are designed using Gephi. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

An Efficient Masked-Face Recognition Model during COVID-19 using Artificial Intelligence Techniques

The COVID-19 is a modern-day crisis unparalleled in its effects leading to an enormous number of sufferers and security problems. People frequently use masks to guard themselves and lessen the spread of the virus. This makes face recognition a very challenging task since certain portions of the face, vital for recognition, are unseen. Face detection has become an important aspect with respect to safety and security and is also widely used in Image processing and Computer Vision. Several new algorithms are being analyzed and researched upon using various convolutional architectures to make the algorithm as efficient and truthful as possible. The crucial attention of researchers during the current COVID-19 situation is to devise means to manage this problem through quick and efficient solutions. These convolutional architectures have made it possible to bring out even the pixel details. The objective is to design a face classifier that can spot any face present in the frame regardless of its alignment, detect the unmasked facial regions, and enhance the recognition accuracy of different masked faces. Copyright © 2022, Anka Publishers. All rights reserved.

Machine learning approach for anxiety and sleep disorders analysis during COVID-19 lockdown.

The Severe Acute Respiratory Syndrome (SARS)-CoV-2 virus caused COVID-19 pandemic has led to various kinds of anxiety and stress in different strata and sections of the society. The aim of this study is to analyse the sleeping and anxiety disorder for a wide distribution of people of different ages and from different strata of life. The study also seeks to investigate the different symptoms and grievances that people suffer from in connection with their sleep patterns and predict the possible relationships and factors in association with outcomes related to COVID-19 pandemic induced stress and issues. A total of 740 participants (51.3% male and 48.7% female) structured with 2 sections, first with general demographic information and second with more targeted questions for each demographic were surveyed. Pittsburgh Sleep Quality Index (PSQI) and General Anxiety Disorder assessment (GAD-7) standard scales were utilized to measure the stress, sleep disorders and anxiety. Experimental results showed positive correlation between PSQI and GAD-7 scores for the participants. After adjusting for age and gender, occupation does not have an effect on sleep quality (PSQI), but it does have an effect on anxiety (GAD-7). Student community in spite of less susceptible to COVID-19 infection found to be highly prone to psychopathy mental health disturbances during the COVID-19 pandemic. The study also highlights the connectivity between lower social status and mental health issues. Random Forest model for college students indicates clearly the stress induced factors as anxiety score, worry about inability to understand concepts taught online, involvement of parents, college hours, worrying about other work load and deadlines for the young students studying in Universities.

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.

Machine learning approach for anxiety and sleep disorders analysis during COVID-19 lockdown

The Severe Acute Respiratory Syndrome (SARS)-CoV-2 virus caused COVID-19 pandemic has led to various kinds of anxiety and stress in different strata and sections of the society. The aim of this study is to analyse the sleeping and anxiety disorder for a wide distribution of people of different ages and from different strata of life. The study also seeks to investigate the different symptoms and grievances that people suffer from in connection with their sleep patterns and predict the possible relationships and factors in association with outcomes related to COVID-19 pandemic induced stress and issues. A total of 740 participants (51.3% male and 48.7% female) structured with 2 sections, first with general demographic information and second with more targeted questions for each demographic were surveyed. Pittsburgh Sleep Quality Index (PSQI) and General Anxiety Disorder assessment (GAD-7) standard scales were utilized to measure the stress, sleep disorders and anxiety. Experimental results showed positive correlation between PSQI and GAD-7 scores for the participants. After adjusting for age and gender, occupation does not have an effect on sleep quality (PSQI), but it does have an effect on anxiety (GAD-7). Student community in spite of less susceptible to COVID-19 infection found to be highly prone to psychopathy mental health disturbances during the COVID-19 pandemic. The study also highlights the connectivity between lower social status and mental health issues. Random Forest model for college students indicates clearly the stress induced factors as anxiety score, worry about inability to understand concepts taught online, involvement of parents, college hours, worrying about other work load and deadlines for the young students studying in Universities.

Intelligent Forecasting Strategy for COVID-19 Pandemic Trend in India: A Statistical Approach

In recent times, an infectious disease namely COVID-19 has affected a large number of individuals. Forecasting models have been helpful in predicting the possible number of confirmed cases, deaths, and recovery counts in the future. In this paper, the prediction of COVID-19 cumulative confirmed cases and deaths for India is analyzed based on various statistical models such as (a) time series, (b) machine learning, and (c) ensemble learning. Autoregressive integrated moving average (ARIMA) and Holt-Winters exponential smoothing in time series;support vector regression (SVR) and linear regression (LR) in machine learning (ML) and random forest regression in ensemble learning (EL) have been implemented for predictions. The accuracies of the trained models are evaluated using metrics such as R-squared value, root mean squared error (RMSE), mean squared error (MSE), mean absolute errors (MAE), and mean absolute percentage error (MAPE). The proposed forecasting models can be used to monitor the rise in COVID-19 cases which can thereby be helpful for government officials to make required changes to their system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Utilization of Transfer Learning Model in Detecting COVID-19 Cases From Chest X-Ray Images

Diagnosis of COVID-19 pneumonia using patients’ chest X-Ray images is new but yet important task in the field of medicine. Researchers from different parts of the globe have developed many deep learning models to classify COVID-19. The performance of feature extraction and classifier plays a vital role in the recognizing the different patterns in the image. The pivotal process is the extraction of optimum features from the chest X-Ray images. The main goal of this study is to design an efficient hybrid algorithm that integrates the robustness of MobileNet (using transfer learning approach) to extract features and Support Vector Machine (SVM) to classify COVID-19. Experiments were conducted to test the proposed algorithm and it was found to have a high classification accuracy of 95%.

Detection of novel coronavirus from chest X-rays using deep convolutional neural networks.

With over 172 Million people infected with the novel coronavirus (COVID-19) globally and with the numbers increasing exponentially, the dire need of a fast diagnostic system keeps on surging. With shortage of kits, and deadly underlying disease due to its vastly mutating and contagious properties, the tired physicians need a fast diagnostic method to cater the requirements of the soaring number of infected patients. Laboratory testing has turned out to be an arduous, cost-ineffective and requiring a well-equipped laboratory for analysis. This paper proposes a convolutional neural network (CNN) based model for analysis/detection of COVID-19, dubbed as CovCNN, which uses the patient's chest X-ray images for the diagnosis of COVID-19 with an aim to assist the medical practitioners to expedite the diagnostic process amongst high workload conditions. In the proposed CovCNN model, a novel deep-CNN based architecture has been incorporated with multiple folds of CNN. These models utilize depth wise convolution with varying dilation rates for efficiently extracting diversified features from chest X-rays. 657 chest X-rays of which 219 were X-ray images of patients infected from COVID-19 and the remaining were the images of non-COVID-19 (i.e. normal or COVID-19 negative) patients. Further, performance evaluation on the dataset using different pre-trained models has been analyzed based on the loss and accuracy curve. The experimental results show that the highest classification accuracy (98.4%) is achieved using the proposed CovCNN model.

Predictive Analytics of COVID-19 Pandemic: Statistical Modelling Perspective

The novel Coronavirus-19 (COVID-19) is an infectious disease and it causes serious lung injury. COVID-19 induces human disease, which has killed numerous people around the world. Moreover, the World Health Organization (WHO) declares this virus as a pandemic and all countries attempt to monitor and control it by locking all places. The illness induces respiratory influenza like problems with symptoms such as cold, cough, fever, and the difficulty of breathing in extremely severe cases. COVID-2019 has been viewed as a global pandemic, and a few analyses are being performed using multiple computational methods to predict the possible development of this pestilence. Considering the various conditions and inquiries these numerical models are based on future tendency. Multiple techniques have been proposed that could be helpful in forecasting the spread of COVID-19. Through statistical modeling on the COVID-19 data, we performed linear regression, random forest, ARIMA and LSTMs, to estimate the empirical indication of COVID-19 ailment and intensity in 4 countries (USA, India, Brazil, and Russia), in order to come up with a better validation. [ABSTRACT FROM AUTHOR] Copyright of Walailak Journal of Science & Technology is the property of Walailak Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Usage of artificial intelligence to prevent and regulate covid-19

Introduction: In this current situation where COVID-19 pandemic has affected the whole world, it is high time to take a step to check the expansion of COVID-19. WHO declared it conversely a pandemic and as an emergency. The clinicians and scientists in medical industries are observing for the technologies with which they can perform screening on the medical images to detect the COVID-19 virus in a person. Objectives: Early identification, screening, acquaintance tracing, prediction, drug/vaccine development and detection with the application of assorted models and algorithms of AI and ML for COVID-19. Methods: Different analyses performed by various researchers using machine learning algorithms and Deep Learning Models using Chest X-rays, Blood Tests have been presented. Different techniques and devices used for acquiring the data through various experiments are also presented. Results: This paper presented a few techniques followed by various researchers;accuracy, sensitivity, recall achieved by the methodologies have been presented. Conclusion: Data collection plays a major role in analyzing the COVID-19 where most of the researches find it very difficult, and early diagnosis also plays a major role in the early detection and control of the widespread of COVID-19. © IJCRR.

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.