Machine learning-based diffusion model for prediction of coronavirus-19 outbreak.

The coronavirus pandemic has been globally impacting the health and prosperity of people. A persistent increase in the number of positive cases has boost the stress among governments across the globe. There is a need of approach which gives more accurate predictions of outbreak. This paper presents a novel approach called diffusion prediction model for prediction of number of coronavirus cases in four countries: India, France, China and Nepal. Diffusion prediction model works on the diffusion process of the human contact. Model considers two forms of spread: when the spread takes time after infecting one person and when the spread is immediate after infecting one person. It makes the proposed model different over other state-of-the art models. It is giving more accurate results than other state-of-the art models. The proposed diffusion prediction model forecasts the number of new cases expected to occur in next 4 weeks. The model has predicted the number of confirmed cases, recovered cases, deaths and active cases. The model can facilitate government to be well prepared for any abrupt rise in this pandemic. The performance is evaluated in terms of accuracy and error rate and compared with the prediction results of support vector machine, logistic regression model and convolution neural network. The results prove the efficiency of the proposed model.

A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques.

COVID-19, a highly infectious respiratory disease a used by SARS virus, has killed millions of people across many countries. To enhance quick and accurate diagnosis of COVID-19, chest X-ray (CXR) imaging methods were commonly utilized. Identifying the infection manually by radio imaging, on the other hand, was considered, extremely difficult due to the time commitment and significant risk of human error. Emerging artificial intelligence (AI) techniques promised exploration in the development of precise and as well as automated COVID-19 detection tools. Convolution neural networks (CNN), a well performing deep learning strategy tends to gain substantial favors among AI approaches for COVID-19 classification. The preprints and published studies to diagnose COVID-19 with CXR pictures using CNN and other deep learning methodologies are reviewed and critically assessed in this research. This study focused on the methodology, algorithms, and preprocessing techniques used in various deep learning architectures, as well as datasets and performance studies of several deep learning architectures used in prediction and diagnosis. Our research concludes with a list of future research directions in COVID-19 imaging categorization.

Artificial Intelligence for the Detection of Coronavirus Disease (COVID-19) from Chest X-Ray Images

The COVID-19 pandemic keeps on devastatingly affecting the wellbeing and prosperity of the worldwide populace. To reduce the rapid spread of the COVID-19 virus primary screening of the infected patient repeatedly is a need. Medical imaging is an essential tool for faster diagnosis to fight against the virus. Early diagnosis on chest radiography shows the Coronavirus disease (COVID-19) infected images shows variations from the Normal images. Deep Convolution Neural Networks shows an outstanding performance in the medical image analysis of Computed Tomography (CT) and Chest X-Ray (CXR) images. Therefore, in this paper, we designed a Deep Convolution Neural Network that detects COVID-19 infected samples from Pneumonia and Normal Chest X-Ray (CXR) images. We also construct the dataset that contains 6023 CXR images in which 5368 images are used for training and 655 images are used for testing the model for the three categories such as COVID-19, Normal, and Pneumonia. The proposed model shows outstanding performance with 97.74% accuracy and 96% average F-Score. The results prove that the model can be used for preliminary screening of the COVID-19 infection using radiological Chest X-Ray (CXR) images to accelerate the treatment for the patients under investigation (PUI) who need it most.Copyright © 2020 Ubiquity Press. All rights reserved.

Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images.

Hundreds of millions of people worldwide have recently been infected by the novel Coronavirus disease (COVID-19), causing significant damage to the health, economy, and welfare of the world's population. Moreover, the unprecedented number of patients with COVID-19 has placed a massive burden on healthcare centers, making timely and rapid diagnosis challenging. A crucial step in minimizing the impact of such problems is to automatically detect infected patients and place them under special care as quickly as possible. Deep learning algorithms, such as Convolutional Neural Networks (CNN), can be used to meet this need. Despite the desired results, most of the existing deep learning-based models were built on millions of parameters (weights), which are not applicable to devices with limited resources. Inspired by such fact, in this research, we developed two new lightweight CNN-based diagnostic models for the automatic and early detection of COVID-19 subjects from chest X-ray images. The first model was built for binary classification (COVID-19 and Normal), whereas the second one was built for multiclass classification (COVID-19, viral pneumonia, or normal). The proposed models were tested on a relatively large dataset of chest X-ray images, and the results showed that the accuracy rates of the 2- and 3-class-based classification models are 98.55% and 96.83%, respectively. The results also revealed that our models achieved competitive performance compared with the existing heavyweight models while significantly reducing cost and memory requirements for computing resources. With these findings, we can indicate that our models are helpful to clinicians in making insightful diagnoses of COVID-19 and are potentially easily deployable on devices with limited computational power and resources.

D-Cov19Net: A DNN based COVID-19 detection system using lung sound.

The limitations of proper detectors for COVID-19 for the proliferating number of patients provoked us to build an auto-diagnosis system to detect COVID-19 infection using only one parameter. Our designed model is based on Deep Convolution Neural Network and considers lung/respiratory sound as the deterministic input for our approach. 'D-Cov19Net' has been trained with 23,592 recordings, begetting an AUC of 0.972 and sensitivity of 0.983 after 100 epochs. The model can be of immense utility in biomedical technology due to its significant accuracy, simplicity, user convenience, feasibility, and faster detection while maintaining social distancing.

Detection of Omicron Caused Pneumonia from Radiology Images Using Convolution Neural Network (CNN)

COVID-19 disease caused by the SARS-CoV-2 virus has created social and economic disruption across the world. The ability of the COVID-19 virus to quickly mutate and transfer has created serious concerns across the world. It is essential to detect COVID-19 infection caused by different variants to take preventive measures accordingly. The existing method of detection of infections caused by COVID-19 and its variants is costly and time-consuming. The impacts of the COVID-19 pandemic in developing countries are very drastic due to the unavailability of medical facilities and infrastructure to handle the pandemic. Pneumonia is the major symptom of COVID-19 infection. The radiology of the lungs in varies in the case of bacterial pneumonia as compared to COVID-19-caused pneumonia. The pattern of pneumonia in lungs in radiology images can also be used to identify the cause associated with pneumonia. In this paper, we propose the methodology of identifying the cause (either due to COVID-19 or other types of infections) of pneumonia from radiology images. Furthermore, because different variants of COVID-19 lead to different patterns of pneumonia, the proposed methodology identifies pneumonia, the COVID-19 caused pneumonia, and Omicron caused pneumonia from the radiology images. To fulfill the above-mentioned tasks, we have used three Convolution Neural Networks (CNNs) at each stage of the proposed methodology. The results unveil that the proposed step-by-step solution enhances the accuracy of pneumonia detection along with finding its cause, despite having a limited dataset. © 2023 Tech Science Press. All rights reserved.

FACE MASK DETECTION AND IDENTIFICATION OF INDIVIDUALS BY USING CNN ALGORITHM

The COVID19 pandemic has had a significant impact on people's social lives. Due to this pandemic, almost every office, institution, organization in the world suffered a great deal from being practically closed. The World Health Organization (W.H.O) recommended everyone wear a mask whenever they step outside or in a public place. Therefore, it is mandatory to cover your face with a mask at public places, social gatherings, etc. Facemask detection has recently become one of the most important tasks to help society. The advancement of technology has proven that deep learning has shown its effectiveness in recognition and classification through image processing. There are many face detection models created by using several algorithms and techniques. Find whether a person has puton a mask properly or not and identify that person who didn’t puton a mask properly employing their age and gender. The combination of the face mask detection module and age & gender detection module is used. In our paper, the Haar cascade classifier was implemented to detect faces from the input images in the face mask recognition module. We train this module using CNN. We can recognize faces in this model using the Voila Jones technique and Haar-like features. The face detection module and age & gender detection module is trained by using a Convolutional neural network. A model trained by Tal Hassner and Gil Levi is used to implement Age and Gender detection;an alert sound will be a part of the outcome if the person is not wearing a mask properly. For the facemask detection module, the dataset is taken from Kaggle;images of people wearing masks and not wearing masks are gathered from different sources and formed into a dataset to train the model. In this paper, we used the Adience dataset to train age & gender detection and a dataset from Kaggle containing pictures of people’s faces with and without a mask. The model attains an accuracy of 93.42 %for face mask detection and an accuracy of 91.23% for Age and Gender detection. © 2006-2022 Asian Research Publishing Network (ARPN). All rights reserved.

Intelligent Sampling of Anterior Human Nasal Swabs using a Collaborative Robotic Arm

Advanced robotics does not always have to be associated with Industry 4.0, but can also be applied, for example, in the Smart Hospital concept. Developments in this field have been driven by the coronavirus disease (COVID-19), and any improvement in the work of medical staff is welcome. In this paper, an experimental robotic platform was designed and implemented whose main function is the swabbing samples from the nasal vestibule. The robotic platform represents a complete integration of software and hardware, where the operator has access to a web-based application and can control a number of functions. The increased safety and collaborative approach cannot be overlooked. The result of this work is a functional prototype of the robotic platform that can be further extended, for example, by using alternative technologies, extending patient safety, or clinical tests and studies. Code is available at https://github.com/ Steigner/ Robo_ Medicinae_ I. © 2022, Brno University of Technology. All rights reserved.

COVIHunt: An Intelligent CNN-Based COVID-19 Detection Using CXR Imaging

Recently, the individuals are under lockdown and limited mobility due to the random spreading of the COVID-19, i.e., coronavirus disease - 2019, worldwide as well as pandemic declared by the World Health Organization (WHO). RT-PCR, i.e., reverse transcriptase-polymerase chain reaction, tests that can detect the RNA from nasopharyngeal swabs have become the norm to allow people to travel within the nation and also to international destinations. This test is people-intensive, i.e., it involves a person collecting the sample, needs transportation with strict precautionary measures, and a lab technician to perform the test which may take up to 2 days to get the results. There is a lot of inconvenience to the people due to this process. Alternatively, X-Ray images have been used primarily by physicians to detect COVID-19 and its severity. Detection of COVID-19 through X-Ray can act as a safe, faster, and alternative method to RT-PCR tests. This method uses a Convolutional Neural Network (CNN) to classify the X-Ray scans into two categories, i.e., COVID-19 positive and negative. In this paper, a novel method named COVIHunt: an intelligent CNN-based COVID-19 detection technique using CXR imaging, is proposed for binary classification. From experiments, it is observed that the proposed work outperforms in comparison with other existing techniques. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A comprehensive tool for rapid and accurate prediction of disease using DNA sequence classifier.

In the current pandemic situation where the coronavirus is spreading very fast that can jump from one human to another. Along with this, there are millions of viruses for example Ebola, SARS, etc. that can spread as fast as the coronavirus due to the mobilization and globalization of the population and are equally deadly. Earlier identification of these viruses can prevent the outbreaks that we are facing currently as well as can help in the earlier designing of drugs. Identification of disease at a prior stage can be achieved through DNA sequence classification as DNA carries most of the genetic information about organisms. This is the reason why the classification of DNA sequences plays an important role in computational biology. This paper has presented a solution in which samples collected from NCBI are used for the classification of DNA sequences. DNA sequence classification will in turn gives the pattern of various diseases; these patterns are then compared with the samples of a newly infected person and can help in the earlier identification of disease. However, feature extraction always remains a big issue. In this paper, a machine learning-based classifier and a new technique for extracting features from DNA sequences based on a hot vector matrix have been proposed. In the hot vector representation of the DNA sequence, each pair of the word is represented using a binary matrix which represents the position of each nucleotide in the DNA sequence. The resultant matrix is then given as an input to the traditional CNN for feature extraction. The results of the proposed method have been compared with 5 well-known classifiers namely Convolution neural network (CNN), Support Vector Machines (SVM), K-Nearest Neighbor (KNN) algorithm, Decision Trees, Recurrent Neural Networks (RNN) on several parameters including precision rate and accuracy and the result shows that the proposed method gives an accuracy of 93.9%, which is highest compared to other classifiers.

Diagnosis of Pneumonia with Chest X-Ray Using Deep Neural Networks

Pneumonia has caused significant deaths worldwide, which remains a threat to human health. Pneumonia is the soreness in the minute air sacs which is originated by deadly lung contagion, which creates congestion by filling the lungs with fluid and pus. People may serve shortness of breath, a cough, fever chest pain, chills, or fatigue which may be a complication of viral infection such as COVID-19 or the flu. World Health Organization (WHO) reports that 33% deaths in India is because of pneumonia. Diagnose of pneumonia needs chest X-rays, CT scans to be evaluated by expert radiotherapists. However, the image quality of chest X-ray has some defects, such as low contrast or overlapping organs and blurred boundary which now has increased the need of automatic system for detecting pneumonia efficiently. The proposed method uses a deep neural network to classify the endless dataset. As deep learning is proven in analyzing medical images, attention of disease classification is grabbed by convolution neural networks (CNNs). Image classification tasks are supported with features learned by pretrained CNN models on extensive datasets. In the proposed work, we evaluate the usefulness of pretrained CNN models used as component extractors followed by various classifiers for the grouping of unusual and typical chest X-rays. Data processing and augmentation are performed as and when required. In the proposed method, CNN is created and trained using tensor flow 2.0. Exactness of 98.4% is accomplished by the suggested weighted classifier model. The anticipated model assists the radiologists for analysis of pneumonia by giving a biting diagnosis. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 PREDICTION USING TRANSFER-LEARNING ON RT-PCR CONFIRMED CXR-IMAGES

Corona-virus is a disease which caused immense destruction to human lives in 21st century. This virus outbreak is considered as an epidemic that spread globally. Crores of people are infected by this virus all over the world. Early detection of the virus is very much important to overcome Covid-19 crisis. This model proposes a convolution neural network model implemented using VGG-19accompanied with Transfer LearningTechnique for the Covid-19 Detection. The Covid-19 dataset considered in this model is a verified report of positive cases confirmed by both RT-PCR and CXR images. Initially, One Hot Encoding Method is used for CXR image data conversion and then pre-processing is done to extract features and then filtered data is forwarded through the VGG-19 and is further processed to Fully Connected Layers. Therefore, the model is later fine-tuned to achieve better classification results. The achieved model accuracy is around 0.94 with a loss is about 0.55. © 2022, Engg Journals Publications. All rights reserved.

Detecting COVID-19 Opacity in X-ray Images Using YOLO and RetinaNet Ensemble

Coronavirus disease(COVID-19) is caused by SARS-COV-2 virus and has been declared as a pandemic. After almost two years of this pandemic, over five million people have lost their lives worldwide due to complications like pneumonia and acute respiratory distress syndrome. Many countries have already witnessed the second wave of pandemic and a huge loss of lives. One way to curb the disease spread is by timely and accurate diagnosis. X-rays and CT-scans can help a radiologist to detect the disease, but detecting COVID-19 on chest radiographs can become challenging as it has similarities with other bacterial and viral pneumonias. Hence, there is a need to develop an algorithm for accurate and fast detection of COVID-19 in a patient. This work showcases the use of object detection deep learning models-You Only Look Once (YOLO) and RetinaNet for accurate localization of regions associated with COVID-19. Proposed method using ensemble of both the models achieves a mean average precision (mAP) score of 0.552, offering an improvement over their individual predictions. © 2022 IEEE.

Covid-19 Classification Using HOG-SVM and Deep Learning Models

COVID-19 is measured as the biggest hazardous and fast infectious grief for the human body which has a severe impact on lives, health, and the community all over the world. It is still spreading throughout the world with different variants which is silently killing many lives globally. Thus, earlier diagnosis and accurate detection of COVID-19 cases are essential to protect global lives. Diagnosis COVID-19 through chest X-ray images is one of the best solutions to detect the virus in the infected person properly and quickly at a low cost. Encouraged by the existing research, in this paper, we proposed a hybrid model to classify the Covid cases and non-Covid cases with chest X-ray images based on feature extraction, machine learning and deep learning techniques. Two feature extractors, Histogram Oriented Gradient (HOG) and CNN (MobileNetV2, Sequential, ResNet152V2) are used to train the model. For the classification, we utilized two approaches: Support Vector Machine (SVM) for machine learning and CNN (MobileNetV2, Sequential, ResNet152V2) classifiers for deep learning. The experimental result analysis shows that the Sequential model and the ResNet152V2 model achieve 100% and 82.6% accuracy respectively which is satisfactory. On the other hand, the HOG-SVM method successfully detects all the test images correctly which provides the best result with 100% accuracy, specificity, and responsiveness over a limited public dataset. © 2022 IEEE.

AI-driven deep convolutional neural networks for chest X-ray pathology identification.

BACKGROUND: Chest X-ray images are widely used to detect many different lung diseases. However, reading chest X-ray images to accurately detect and classify different lung diseases by doctors is often difficult with large inter-reader variability. Thus, there is a huge demand for developing computer-aided automated schemes of chest X-ray images to help doctors more accurately and efficiently detect lung diseases depicting on chest X-ray images. OBJECTIVE: To develop convolution neural network (CNN) based deep learning models and compare their feasibility and performance to classify 14 chest diseases or pathology patterns based on chest X-rays. METHOD: Several CNN models pre-trained using ImageNet dataset are modified as transfer learning models and applied to classify between 14 different chest pathology and normal chest patterns depicting on chest X-ray images. In this process, a deep convolution generative adversarial network (DC-GAN) is also trained to mitigate the effects of small or imbalanced dataset and generate synthetic images to balance the dataset of different diseases. The classification models are trained and tested using a large dataset involving 91,324 frontal-view chest X-ray images. RESULTS: In this study, eight models are trained and compared. Among them, ResNet-152 model achieves an accuracy of 67% and 62% with and without data augmentation, respectively. Inception-V3, NasNetLarge, Xcaption, ResNet-50 and InceptionResNetV2 achieve accuracy of 68%, 62%, 66%, 66% and 54% respectively. Additionally, Resnet-152 with data augmentation achieves an accuracy of 83% but only for six classes. CONCLUSION: This study solves the problem of having fewer data by using GAN-based techniques to add synthetic images and demonstrates the feasibility of applying transfer learning CNN method to help classify 14 types of chest diseases depicting on chest X-ray images.

Face Mask Detection with Computer Vision Deep Learning

Computer vision has been in high demand due to the Coronavirus pandemic to improve healthcare sector. During this time detecting small objects is a tougher task, as it uses both classification and detection using video illustration. This Object Detection demonstrated a superior feature ie, Mask Detection compared to other object detection models. This Face mask detection using YOLOv3 performed well. This Face mask detection measures performance at the same time with strong GPU and works with less computation power We add dataset which consists of both people wearing face masks and without facemask, The model is trained by this dataset consisting of face mask and no face mask. Real time video can also be used to verify whether the person is wearing mask or not. This Face Mask Detection model attained good output with 96% classification. © 2021 IEEE.

COVID-19 Prevention Melioration: Face Mask and Social Distancing Detection

The pandemic Coronovirus disease 2019 (COVID-19) gave rise to a huge impact on everyone's lives. Millions of people have been infected worldwide. Now the vaccination has been found, WHO has still insisted on wearing mask and maintaining social distance is necessary as it is the most feasible way to prevent ourselves from the virus. This notion motivated me to contribute an efficient computer vision based detection system to public safety.The detection system detects if there is any violations through the camera and or on any video or image input. In this system, we have used deep learning algorithm. This system I have proposed here can be applied in places where people gather in huge amount. This automated detection technique makes it easier to inspect the public gathering in malls, streets, any firms, apartments, etc. © 2021 IEEE.

Building an AI Model on ECG Data for Identifying Burnout/Stressed Healthcare Workers Involved in Covid-19 Management

An electrocardiogram (ECG) is used to monitor electrical activity of the heart. ECG data with 12 leads can help in detecting various cardiac (heart) problems. One of the significant factors that contribute to various cardiac diseases is work/personal stress. Use of various machine and deep learning approaches to analyse ECG data has yielded promising results in the field of predictive and diagnostic healthcare with less human error or bias. In our study, 10sec of 500Hz, 12-lead ECG samples were collected from the healthcare workers, who were involved directly or indirectly in taking care of COVID-19 patients. The present study was designed to determine whether Healthcare workers were stressed by using only ECG as input to a deep learning model. To the best of our knowledge, no earlier ECG based study has been carried out to identify stressed persons among the healthcare workers who are giving support to COVID-19 patients. In this study, ECG data of healthcare workers giving services to COVID-19 patients is utilized. This data was collected from four tertiary academic care centres of India. A modified version of AlexNet is utilized on this data that is able to identify a stressed healthcare worker with 99.397% accuracy and 99.411% AUC score. Successful deployment of such systems can help governments and hospital administrations make appropriate policy decisions during pandemics. © 2021 IEEE.

Accurate Prognosis Of Covid-19 Using Ct Scan Images With Deep Learning Model And Machine Learning Classifiers

The Covid-19 disease is caused by coronavirus or SARS-CoV-2 has wrecked havoc globally. This epidemic severely impacted the economy of most of the countries across the world and has taken away many lives. To control the pandemic situation many researchers, organizations, and institutes have come up with the pathogenesis and developing vaccines to decimate this disease. Out of the several techniques, one of the techniques use image patterns on Computed Tomography (CT) to detect whether a patient is Covid-19 positive or not. In this work, the SARS-COV-2 dataset has been used for the detection of Covid-19 images and normal images. These dataset images have been fed to various deep learning models for extracting the features and finally passed to various ML classifiers which classify the images as Covid-19 or normal images. The results have established that the VGG19 model along with Logistic Regression (LR) classifier gives the maximum AUC and accuracy of 98.5% and 94.6% © 2021. Indian Journal of Radio and Space Physics.All Rights Reserved.

UBNet: Deep learning-based approach for automatic X-ray image detection of pneumonia and COVID-19 patients.

BACKGROUND: Analysis of chest X-ray images is one of the primary standards in diagnosing patients with COVID-19 and pneumonia, which is faster than using PCR Swab method. However, accuracy of using X-ray images needs to be improved. OBJECTIVE: To develop a new deep learning system of chest X-ray images and evaluate whether it can quickly and accurately detect pneumonia and COVID-19 patients. METHODS: The developed deep learning system (UBNet v3) uses three architectural hierarchies, namely first, to build an architecture containing 7 convolution layers and 3 ANN layers (UBNet v1) to classify between normal images and pneumonia images. Second, using 4 layers of convolution and 3 layers of ANN (UBNet v2) to classify between bacterial and viral pneumonia images. Third, using UBNet v1 to classify between pneumonia virus images and COVID-19 virus infected images. An open-source database with 9,250 chest X-ray images including 3,592 COVID-19 images were used in this study to train and test the developed deep learning models. RESULTS: CNN architecture with a hierarchical scheme developed in UBNet v3 using a simple architecture yielded following performance indices to detect chest X-ray images of COVID-19 patients namely, 99.6%accuracy, 99.7%precision, 99.7%sensitivity, 99.1%specificity, and F1 score of 99.74%. A desktop GUI-based monitoring and classification system supported by a simple CNN architecture can process each chest X-ray image to detect and classify COVID-19 image with an average time of 1.21 seconds. CONCLUSION: Using three hierarchical architectures in UBNet v3 improves system performance in classifying chest X-ray images of pneumonia and COVID-19 patients. A simple architecture also speeds up image processing time.