Medical Image Diagnosis Using Deep Learning Classifiers for COVID-19

COVID-19 and other diseases must be precisely and swiftly classified to minimize disease spread and avoid overburdening the healthcare system. The main purpose of this study is to develop deep-learning classifiers for normal, viral pneumonia, and COVID-19 disorders using CXR pictures. Deep learning image classification algorithms are used to recognize and categorise image data to detect the presence of illnesses. The raw image must be pre-processed since deep neural networks perform the most important aspect of medical image identification, which includes translating the raw image into an intelligible format. The dataset includes three classifications, including normal and viral pneumonia and COVID-19. To aid in quick diagnosis and the proposed models leverage the performance validation of several models, which are summarised in the form of a recall, Fl-score, precision, accuracy, and AUC, to distinguish COVID-19 from other types of pneumonia. When all the deep learning classifiers and performance parameters were analyzed, the ResNetl0lV2 achieved the highest accuracy of COVID-19 classifications is 97.S2%, ResNetl0lV2 had the greatest accuracy of the normal categorization is 92.04% and the Densenet201 had the greatest accuracy of the pneumonia classification is 99.92%. The suggested deep learning system is an excellent choice for clinical use to aid in the COVID-19, normal, and pneumonia processes for diagnosing infections using CXR scans. Furthermore, the suggested approaches provided a realistic technique to implement in real-world practice, assisting medical professionals in diagnosing illnesses from CXR images. © 2023 IEEE.

Detection of COVID-19 Infection Using Convolutional Neural Network

Coronavirus disease (COVID-19) is a newly discovered viral sickness that can be fatal. The majority of patients will experience mild to severe respiratory problems and will improve without need for special treatment. Persons over 65, and for those who are underlying medical disorders such cardiovascular disease, asthma, respiratory illness, and cancer, are more prone for developing severe symptoms. In these conditions, 3D volumetric imaging has proven to be a useful technique for COVID-19 patient diagnosis and prognosis. We present a new approach for detecting and classifying COVID-19 infection using 3D volumetric lung imaging in this work. For the detection and classification process, we have used 3D volumetric image processing and deep learning techniques, respectively. Early recognition and finding are basic elements to stop COVID-19 spreading. Various profound learning-based approaches had been proposed for COVID-19 separating CT examines as an instrument to computerize and assist with finding. These methods suffer with at least one of the faults listed below: (i) They treat each CT scan individually (ii) These methods are trained and tested on the same dataset. To address these two challenges, we present an accurate deep learning technique for COVID-19 screening using a democratic framework in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Real-Time Face Mask Detection to Ensure COVID-19 Precautionary Measures

The rapid spread of COVID-19 (Coronavirus 2019) around the globe has just brought about a serious public health emergency. The World Health Organization (WHO) has distributed a number of different guidelines in an effort to limit the spread of COVID-19. If a someone is concerned about getting COVID-19, the World Health Organization advises that they wear a mask whenever they are in a public or crowded place. This recommendation applies to both adults and children. Simply looking at someone makes it hard to tell if they are concealing their identity with a mask. In this study, we conduct a comprehensive analysis of the data that was collected and the performance is being measured with deep learning architectures.In this research work, each and every one of the prerequisites for such a model was investigated. The suggested approach uses a deep learning technique-CNN in order to differentiate between labels in an image that have masks and labels that do not have masks. The results of the experiments show that the proposed system achieves 99.77% accuracy on the benchmark datasets, exceeding previous systems and datasets that are considered state-of-the-art in a real-time setting. Copyright © 2022, Anka Publishers. All rights reserved.

Detection of COVID-19 Infection Using Convolutional Neural Network

Coronavirus disease (COVID-19) is a newly discovered viral sickness that can be fatal. The majority of patients will experience mild to severe respiratory problems and will improve without need for special treatment. Persons over 65, and for those who are underlying medical disorders such cardiovascular disease, asthma, respiratory illness, and cancer, are more prone for developing severe symptoms. In these conditions, 3D volumetric imaging has proven to be a useful technique for COVID-19 patient diagnosis and prognosis. We present a new approach for detecting and classifying COVID-19 infection using 3D volumetric lung imaging in this work. For the detection and classification process, we have used 3D volumetric image processing and deep learning techniques, respectively. Early recognition and finding are basic elements to stop COVID-19 spreading. Various profound learning-based approaches had been proposed for COVID-19 separating CT examines as an instrument to computerize and assist with finding. These methods suffer with at least one of the faults listed below: (i) They treat each CT scan individually (ii) These methods are trained and tested on the same dataset. To address these two challenges, we present an accurate deep learning technique for COVID-19 screening using a democratic framework in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Face Mask and Social Distance Monitoring via Computer Vision and Deployable System Architecture

The coronavirus (COVID-19) is a lethal virus causing a rapidly infec-tious disease throughout the globe. Spreading awareness, taking preventive mea-sures, imposing strict restrictions on public gatherings, wearing facial masks, and maintaining safe social distancing have become crucial factors in keeping the virus at bay. Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus, the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly. To fight the spread of this virus, technologically developed systems have become very useful. However, the implementation of an automatic, robust, continuous, and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community. This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system. A modified version of a convolutional neural network, the ResNet50 model, has been utilized to identify masked faces in peo-ple. You Only Look Once (YOLOv3) approach is applied for object detection and the DeepSORT technique is used to measure the social distance. The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system, Jetson Nano edge computing device, and smartphones, Android and iOS applications. Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores. © 2023, Tech Science Press. All rights reserved.

Deep Learning-Based Lung Infection Detection Using Radiology Modalities and Comparisons on Benchmark Datasets in COVID-19 Pandemic

The SARS-CoV-2 (severe acute respiratory syndrome coronavirus) pandemic, also known as COVID-19 (coronavirus 2019), impacted humanity worldwide and significantly impacted the healthcare community. COVID-19 infection and transmission have resulted in several international issues, including health hazards. Sore throat, trouble breathing, cough, fever, weariness, and other clinical signs have been described. In SARS-CoV-2 patients, the most common infections are in the lungs and the gastric intestine. Lung infections may be caused by viral or bacterial infections, physical trauma, or inhalation of harmful particles. This research presents deep learning-based approaches for COVID-19 infection detection based on radiological images, prevention and therapy based on benchmark publicly available datasets. Finally, the analysis and findings explore evidence-based methodologies and modalities, leading to a conclusion and possible future healthcare planning. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Prediction of Flight Fare using Deep Learning Techniques

Due to the drastic growth in aviation sector, population living standard and adverse effect of the Covid-19 pandemic there is an increase in air travel in the recent years. However, Indian airline corporation use revenue management system to make real-time price adjustments, causing fare to fluctuate considerably. The price is characterized by considerable fluctuation, making it viable to conduct research on price prediction. This issue is addressed in this work where flight fare prediction is carried out by implementing deep neural network system. Furthermore, the attributes of the given dataset have been analyzed using various visualization technique such as correlation matrix, boxplot, bar chart and line plot. The results justify that the random forest and the gradient boos technique gives highest accuracy with the fare prediction dataset. © 2022 IEEE.

Comparative Analysis of YOLOv4 and YOLOv4-tiny Techniques towards Face Mask Detection

Covid-19 has brought various complications in our day-to-day life leading to a disruption in overall movements across the world. Although still researchers and scientists are working on finding more effective ways to deal with it, wearing a face is one of the most simplistic yet efficient ways to overcome this. Wearing a face mask all the time in public places has become a new normal. Therefore, face mask detection for monitoring of people in public places has become a crucial task. Deep learning has been used to make recent advances in the field of object detection. To accomplish this objective, this research employs three state-of-the-art object identification models, notably YOLOv4 and YOLOv4-tiny. The models were trained using a dataset that included photos of persons wearing and not wearing masks. Considering it for surveillance purposes, it can also be used for detection of face and mask in motion. The models employ an approach that involves drawing bounding boxes (red or green) around people's faces and determining whether or not they are wearing a face mask. Further, the performance of these models was compared using mAP, recall F1-score and FPS © 2021 IEEE.

Covid Alert System: A Smart Security System to Alert Violations of Covid Protocol Using OpenCV

According to the World Health Organization, the COVID-19 pandemic is causing overall prosperity into crisis. The pandemic made all countries across the world commence lockdowns to decrease the transfer of the virus. The protection procedure is wearing a face mask in open areas. Records show that wearing face masks in crowded areas diminishes the threat of virus transfer. A novel framework is proposed in this paper to minimize the spreading of COVID-19 in crowded places like schools, malls, theaters, etc., by identifying individuals without wearing a mask. A face masks algorithm and CNN algorithm have been developed the usage of machine learning which acknowledges the faces besides masks and signals the safety system. It can detect face masks with high accuracy of 99%. The methodology proposed exhibits its high effectiveness in detecting facial masks. © 2022, Springer Nature Switzerland AG.