Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection

This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and Interventional Radiology (SIRM), Radiopaedia, The Cancer Imaging Archive (TCIA) and Kaggle repositories were taken. A multi-approach utilizing deep learning ResNet101 with and without hyperparameters optimization was employed. Additionally, the fea-tures extracted from the average pooling layer of ResNet101 were used as input to machine learning (ML) algorithms, which twice trained the learning algorithms. The ResNet101 with optimized parameters yielded improved performance to default parameters. The extracted features from ResNet101 are fed to the k-nearest neighbor (KNN) and support vector machine (SVM) yielded the highest 3-class classification performance of 99.86% and 99.46%, respectively. The results indicate that the proposed approach can be bet-ter utilized for improving the accuracy and diagnostic efficiency of CXRs. The proposed deep learning model has the potential to improve further the efficiency of the healthcare systems for proper diagnosis and prognosis of COVID-19 lung infection.

Predicting Intensive Care Unit Admission Using Machine and Deep Learning: COVID-19 Case Study

The COVID-19 pandemic has affected the entire world by causing widespread panic and disrupting normal life. Since the outbreak began in December 2019, the virus has killed thousands of people and infected millions more. Hospitals are struggling to keep up with large patient flows. In some situations, hospitals are lacking enough beds and ventilators to accommodate all of their patients or are running low on supplies such as masks and gloves. Predicting intensive care unit (ICU) admission of patients with COVID-19 could help clinicians better allocate scarce ICU resources. In this study, many machine and deep learning algorithms are tested over predicting ICU admission of patients with COVID-19. Most of the algorithms we studied are extremely accurate toward this goal. With the convolutional neural network (CNN), we reach the highest results on our metrics (90.09% accuracy and 93.08% ROC-AUC), which demonstrates the usability of these learning models to identify patients who are likely to require ICU admission and assist hospitals in optimizing their resource management and allocation during the COVID-19 pandemic or others. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Deep Learning for Detection and Prediction of Covid-19 Virus on CT-Scan Image Dataset

As we all know, COVID-19 appears to be having a terrible impact on world health and well-being. Furthermore, at its peak, the COVID-19 cases worldwide reached a huge number i.e., in millions. The objective of the present work is to develop a model that detects COVID-19 utilizing CT-Scan Image Dataset and DL Techniques. As the number of verified cases rises, it becomes more critical to monitor and precisely categorize healthy and infected people. RT-PCR testing is the most used approach for the detection of Covid-19. However, several investigations have found that it has a low sensitivity in the early stages. Computer tomography (CT) is also used to detect image-morphological patterns of COVID-19-related chest lesions. The RT-PCR technique for diagnosing COVID has some drawbacks. For starters, test kits are insufficiently available, necessitating greater testing time and the sensitivity of testing varies. Therefore, employing CT scan pictures to screen COVID-19 is essential. The results showed that CT scan pictures might efficiently identify COVID-19, saving more lives. A Convolutional Neural Network (CNN) is a sort of Artificial Neural Network that is commonly used for image/object detection with class. An Input layer, Hidden layers, and an Output layer are common components of a neural network (NN). CNN is inspired by the brain's architecture. Artificial neurons or nodes in CNNs, like neurons in the brain, take inputs, process them, and deliver the result as output. Illness severity can be detected and calculated for future scopes and research. Another challenge encountered when dealing with severity infection detection and extending the existing work by using frameworks in order to increase accuracy. The proposed ECNN technique outperformed than CNN in terms of accuracy (95.35), execution time, and performance. This study could be extended or improved in the future by directing severity identification on the CT-Scan image dataset. © 2023 IEEE.

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.

A multimodal AI-based non-invasive COVID-19 grading framework powered by deep learning, manta ray, and fuzzy inference system from multimedia vital signs.

The COVID-19 pandemic has presented unprecedented challenges to healthcare systems worldwide. One of the key challenges in controlling and managing the pandemic is accurate and rapid diagnosis of COVID-19 cases. Traditional diagnostic methods such as RT-PCR tests are time-consuming and require specialized equipment and trained personnel. Computer-aided diagnosis systems and artificial intelligence (AI) have emerged as promising tools for developing cost-effective and accurate diagnostic approaches. Most studies in this area have focused on diagnosing COVID-19 based on a single modality, such as chest X-rays or cough sounds. However, relying on a single modality may not accurately detect the virus, especially in its early stages. In this research, we propose a non-invasive diagnostic framework consisting of four cascaded layers that work together to accurately detect COVID-19 in patients. The first layer of the framework performs basic diagnostics such as patient temperature, blood oxygen level, and breathing profile, providing initial insights into the patient's condition. The second layer analyzes the coughing profile, while the third layer evaluates chest imaging data such as X-ray and CT scans. Finally, the fourth layer utilizes a fuzzy logic inference system based on the previous three layers to generate a reliable and accurate diagnosis. To evaluate the effectiveness of the proposed framework, we used two datasets: the Cough Dataset and the COVID-19 Radiography Database. The experimental results demonstrate that the proposed framework is effective and trustworthy in terms of accuracy, precision, sensitivity, specificity, F1-score, and balanced accuracy. The audio-based classification achieved an accuracy of 96.55%, while the CXR-based classification achieved an accuracy of 98.55%. The proposed framework has the potential to significantly improve the accuracy and speed of COVID-19 diagnosis, allowing for more effective control and management of the pandemic. Furthermore, the framework's non-invasive nature makes it a more attractive option for patients, reducing the risk of infection and discomfort associated with traditional diagnostic methods.

Application Of Neural Network For The Detection Of Covid-19 Or Viral Pneumonia

A bacterial infection in the lungs can cause viral pneumonia, a disease. Later the middle of December 2019, there have been multiple episodes of pneumonia in Wuhan City, China, with no known cause;it has since been discovered that this pneumonia is actually a new respiratory condition brought on by coronavirus infection. Humans who have lung abnormalities are more likely to develop high-risk conditions;this risk can be decreased with much quicker and more effective therapy. The symptoms of Covid-19 pneumonia are similar to those of viral pneumonia;they are not distinctive. X-ray or Computed Tomography (CT) scan images are used to identify lung abnormalities. Even for a skilled radiologist, it might be challenging to identify Covid-19/Viral pneumonia by looking at the X-ray images. For prompt and effective treatment, accurate diagnosis is essential. In this epidemic condition, delayed diagnosis can cause the number of cases to double, hence a suitable tool is required is necessary for the early identification of Covid-19. This paper highlights various AI techniques as a part of our contribution to swift identification and curie Covid-19 to front-line corona. The safety of Covid-19 people who have viral pneumonia is a concern. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), two AI technologies from Deep Learning (DL), were utilized to identify Covid-19/Viral pneumonia. The Algorithm is taught utilizing non-public local hospitals or Covid-19 wards, as well as X-ray images of healthy lungs, fake lungs from viral pneumonia, and ostentatious lungs from Covid-19 that are all publicly available. The model is also validated over a lengthy period of time using the transfer learning technique. The results correspond with clinically tested positive Covid-19 patients who underwent Swap testing conducted by medical professionals, giving us an accuracy of 78 to 82 percent. We discovered that each DL model has a unique expertise after testing the various models. [ FROM AUTHOR] Copyright of Journal of Pharmaceutical Negative Results is the property of ResearchTrentz 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 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 . (Copyright applies to all s.)

Covid-19 or Viral Pneumonia Detection Using AI Tools

Viral pneumonia is a disease which occurs in lungs due to bacterial infection. Since middle of December 2019, many cases of pneumonia with unknown cause were found in Wuhan City, China;at present, it has been confirmed that it is a new respiratory disorder caused due to coronavirus infection. Lungs abnormality is highly risky condition in humans;the reduction of the risk is done by enabling quick and efficient treatment. The Covid-19 pneumonia is mimicking viral pneumonia, that is, their symptoms are undistinguished. Lung's abnormality is detected by Computed Tomography (CT) scan images or X-ray images. By viewing the X-rays or CT scan images, even for a well-trained radiologist, it is difficult to detect Covid-19/viral pneumonia. For quick and efficient treatment, it is necessary that proper detection must take place and during this epidemic situation, late detection can lead to doubling of cases;hence, there is a need of proper tool for quick detection of Covid-19/viral pneumonia. This chapter is discussing various AI tools for quick detection as a part of our contribution for quick detection and cure of Covid-19 to front line corona worriers and safety of viral pneumonia patients from Covid-19. The two AI tools are from deep learning (DL), that is, Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN), which are used for the detection of Covid-19/viral pneumonia. The algorithm is trained using available X-ray images of health lungs, viral pneumonia-affected lungs, and Covid-19-affected lungs available through Kaggle and nondisclosed local hospitals or Covid-19 wards. Also transfer learning method is also used for long-lasting validation of the model. The results give us an accuracy for CNN 83.2 to 94.1% results which are also matched with practically tested positive Covid-19 patients using swab tests by doctors. After testing the various models, we also came through that every model of DL has its own specialty. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

FV-Seg-Net: Fully Volumetric Network for Accurate Segmentation of COVID-19 Lesions From Chest CT Scans

Automated and precise pneumonia segmentation of COVID-19 extends the view of medical supply chains and offers crucial medical supplies to fight the COVID-19 pandemic. Deep learning plays a vital role in improving the COVID-19 segmentation from computed tomography (CT) scans. However, the literature lacks a precise segmentation approach on small-size lesions because they often split the CT scan into 2-D slices or 3-D patches, leading to the loss of contextual and/or global information. In order to address this, this article proposes a novel fully volumetric segmentation network, called FV-Seg-Net, that effectively exploits the local and global spatial information and enables the entire CT volume processing at once. The decoder is designed using a computationally efficient recalibrated anisotropic convolution module that can acquire the 3-D semantic representation of the CT volumes with anisotropic resolution. To avoid losing information during down-sampling, we reconstruct the skip-connection using a multilevel multiscale pyramid aggregation module and ensure more effective context fusion that improves the reconstruction capability of the decoder. Finally, stacked data augmentation (StackAug) is presented to magnify the training data and improve the generalizability of FV-Seg-Net. Proof of concept experiments on two public datasets demonstrates that the FV-Seg-Net achieves excellent segmentation performance (Dice score: 85.69 and a surface-dice: 84.79%), outperforming the current cutting-edge studies.

Fast Hybrid Deep Neural Network for Diagnosis of COVID-19 using Chest X-Ray Images

In the last three years, the coronavirus (COVID-19) pandemic put healthcare systems worldwide under tremendous pressure. Imaging techniques, such as Chest X-Ray (CXR) images, play an essential role in diagnosing many diseases (for example, COVID-19). Recently, intelligent systems (Machine Learning (ML) and Deep Learning (DL)) have been widely utilized to identify COVID-19 from other upper respiratory diseases (such as viral pneumonia and lung opacity). Nevertheless, identifying COVID-19 from the CXR images is challenging due to similar symptoms. To improve the diagnosis of COVID-19 using CXR images, this article proposes a new deep neural network model called Fast Hybrid Deep Neural Network (FHDNN). FHDNN consists of various convolutional layers and various dense layers. In the beginning, we preprocessed the dataset, extracted the best features, and expanded it. Then, we converted it from two dimensions to one dimension to reduce training speed and hardware requirements. The experimental results demonstrate that preprocessing and feature expansion before applying FHDNN lead to better detection accuracy and reduced speedy execution. Furthermore, the model FHDNN outperformed the counterparts by achieving an accuracy of 99.9%, recall of 99.9%, F1-Score has 99.9%, and precision of 99.9% for the detection and classification of COVID-19. Accordingly, FHDNN is more reliable and can be considered a robust and faster model in COVID-19 detection. © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Model for movement of individuals in society after the corona pandemic using deep learning algorithms

The health crisis that attributed to the rapid spread of the COVID-19 has impacted the globe negatively in terms of economy, education and transport and led to the global lockdown. The risk of the COVID-19 infection has been increased due to a lack of successful cure for the disease. Thus, social distancing is considered as the most appropriate precaution measureto control the viral spread throughout the world. Social distancing means that physical contact between individuals can be prevented to reduce the viral transmission effectively. The purpose of this work is to provide a deep learning model capable of predicting the movement of people in the pandemic to take precautions and control the COVID-19 infection. This model is based on twoLSTMand GRU algorithms. The results show that the GRU is better than LSTM in terms of prediction error rate and duration. © 2023 Author(s).

A Comparison of COVID-19 Detection using Deep Learning Methods

The recognition of covid-19 is major confront in today's world, specified as sudden increase in spreading of the disease. Hence, identifying this infection in earlier phase facilitates medicinal fields such as doctors, nurses and lab reporters. This article introduces a novel deep learning technique especially Convolutional Neural Network (CNN) by analyzing features in chest input images. Moreover, this proposed Convolutional Neural Network detects the covid-19 disease under several layers and finally performs binary classification that categorizes input images into covid 19 and non-covid patients. Finally, comparisons had made among all models to predict which model diagnose the disease accurately. To evaluate the overall model performance in detection and classification of covid disease, metrics criterias precision, recall and F1-score are evaluated. Validation analysis were completed for quantify the outcomes via performance measures for each model. This proposed comparison attains maximum accuracy of 100% along with least loss as 0.04 that might diminish human inaccuracy in identification procedure. © 2023 IEEE.

A Hybrid technolgy using Machine learning and blockchain technology to prevent Covid

Since the year 2020, there has been an outbreak of the respiratory infection that caused a high peak mortality rate, which has led to an increase in the prevalence of Covid. The unanticipated development of the COVID-19 sickness as well as its unchecked global spread show the limitations of the currently available healthcare systems in their ability to respond to emergencies that harm the general population's health. As a result of cutting-edge technology like AI and biological computing (BC) these issues treated promisingly for the covid pandemic. In particular, BC assist in early detection to aid in the fight against pandemics. With the protocols that have been put in place to avoid infections, including the use of masks, social isolation within a radius of 6 meters, routine testing, and two doses of vaccinations. This system comprises the detection of masks, people, and temperatures, as well as the monitoring of information, tracking of in-person contact, and the present state of a person's medical record. Diseases are now able to be traced, and their transmission can be stopped, thanks to advances in technology and the growing prevalence of smartphone use. Because of the reopening of more economic sectors and the continuous widespread distribution of Covid, it is even more important to ensure that you adhere to the provided instructions in order to avoid contracting an infection. © 2023 IEEE.

Deep learning: An approach to encounter pandemic effect of novel corona virus (COVID-19)

Researchers around the world are struggling to discover ground-breaking equip-ment aimed at building a great healthcare structure to fight the novel corona virus for the duration of this global epidemic. How deep learning (DL) encountered the COVID-19 epidemic and what are the current guidelines for exploring the potential in COVID-19 are the subject to walk around. Over time, genetic material of novel corona viruses mutates itself and changed its characteristics to create different vari¬ants of viruses. These distinctive variants can trigger different waves of destructive infection in different parts of world. The substantiation of DL pertinences on the precedent pandemic motivates the professionals by giving an innovative trend to organize this outburst to make it least effective. The main target of this article is to study the utility of deep learning-based approaches on COVID-19 and also their credibility in terms of containment of the pandemic based on recent works around the globe. The study has listed down recent works within DL approaches regarding marking out of virus-affected people, investigation of its protein formation, vaccine & medicine finding, virus relentlessness, and contamination to direct the enduring eruption. DL is endowed with a suitable contrivance intended for rapid selection COVID-19 along with pronouncement possible high-risk patients, which possibly will be cooperative for medical resource optimization and early prevention prior to patients suffering rigorous indication. In this study, the wide-ranging consequence of DL on several magnitudes to be in command of novel coronavirus (COVID-19) is discussed, and attempts are made to investigate it. Despite rich studies being con¬ducted through DL algorithms, there are still many limitations and contradictions in the area of COVID research. The continuous evolution of DL on coronavirus handles contamination and is costly to create the right resolution task. Apart from this, in this work, a DL-based pandemic analysis has been done using the received dataset from about 55 hospitals in West Bengal, India. According to some research scientists, we may enter the third and fourth waves too, thus this work will be help¬ful for further research activity in the years to come. Finally, it is expected this work will help many researchers throughout the world get some opportunity to find out the final remedy to get rid of this deadly virus. © 2023 Scrivener Publishing LLC. All rights reserved.

Concepts and techniques in deep learning applications in the field of IoT systems and security

Deep learning (DL), a subdivision of machine learning (ML), i.e., an integral part of artificial intelligence used in various applications in today's life. At present, machine learning approach is almost completely dependent on DL techniques, which produce accurate results with the help of human centric nature of learning. It has gone off in the community awareness, mostly as extrapolative and analyt-ical products that saturate our planet in most useful, organized, and time- and cost-competent method of ML approach. There are some algorithms, like genera¬tive adversarial networks, multilayer perceptions, convolution neural networks, or self-organizing maps, that have entirely changed the thinking toward information processing means. Currently, DL is using in numerous domains like knowledge, commerce, science, administration sectors;it can be employed on novel corona virus prediction, detection, and analysis of clinical and method logical character¬istics too is also a matter of discussion here. Our work is absolutely displays on the notion of crucial sophisticated design, method, inspirational characteristics and constraint of DL. This writing section describes a detailed analysis of chronolog¬ical and modern trailblazing approaches to the distribution of conjecture, myth, and text;social network analysis;and innovative advances in natural language pro¬cessing, extensive research around spin, and in-depth learning activities. The main target of this work is to describe the newly developed DL techniques for Internet of Things (IoT) architecture and its security. IoT security threats associated with the underlying or newly introduced threat are talked about and diverse possible IoT system attacks and probable threats connected to all facets are thrashed out. The possibilities, advantages, and limitations of both systems are illustrated systematically by analyzing the DL strategy aimed at IoT security. We provide perspectives and related issues regarding IoT security from ML/DL. Discussed approaches and problems of potential expectations can serve as research guide-lines for the future endeavor. © 2023 Scrivener Publishing LLC. All rights reserved.

IoT & ML-Based Healthcare Monitoring System-Review

Intelligent medical management is one of several modern city and society management fields where the Internet of Things (IoT) is essential. Smart cities' current engagement between technology and the health care system is strengthened by the intelligent IoT's limitless networking capabilities for big data analysis in medicine. Allows for more practical methods for efficiently monitoring patients' health and providing medical services remotely online assessment of patients' health status by doctors, nurses, and other healthcare professionals. The of the current study aims to provide a full examination of the function of IoT in medical management systems, analyse the available concerns, and address many of the open questions. It also aims to provide an up-to-date and comprehensive review of this field. Enabling technology and hints at a variety of uses There have been suggested research plans. The following are some examples of IoT applications from previous studies: wearable technology, monitoring technology, rehabilitation technology, telehealth, behaviour modification, smart city, and smart home. This comprehensive review identifies the crucial elements that make it possible to comprehend the healthcare possibilities and obstacles providers to put IoT applications into action. Lastly, anticipated COVID-19 effects on IoT uptake this review assessed in the field of healthcare. © 2022 IEEE.

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.

An effective classification of Skin Disease using Deep Learning Techniques

According to WHO, Skin Infection is very common but sometimes very serious and affects a large no population all over the world. Monkeypox, Chickenpox, and Measles are the major infectious disease that causes skin infection all over the world. It has been obverse that the cases of Monkeypox have drastically increased as an effect of Covid 19. This infection has spread easily and exponentially that cause serious health issues in many underdeveloped and developing countries. Some time it has been observed that people are not able to properly classify the type of skin infection well in time, which can be a main reason of serious health issues. So, it became important to propose an effective classification of Skin Disease. In this paper the authors have proposed an effective classification of Skin Disease using Deep Learning Techniques. This approach will help in classification of chicken pox, measles, and monkeypox through skin images. The authors have utilized Monkeypox Skin Images Dataset (MSID) dataset to apply the proposed approach. The Loss, Accuracy, Precision, Recall, AUC, and F1 Score parameters have been used to analyze the performance of proposed approaches. The best algorithms with maximum accuracy and other parameters are Xception, EfficientNetV2L, and EfficientNetV2M, and CNN, VGG16, and VGG19 are the least favored algorithms for this research. © 2023 IEEE.

ADPT: An Automated Disease Prognosis Tool Towards Classifying Medical Disease Using Hybrid Architecture of Deep Learning Paradigm

The Covid 19 beta coronavirus, commonly known as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently one of the most significant RNA-type viruses in human health. However, more such epidemics occurred beforehand because they were not limited. Much research has recently been carried out on classifying the disease. Still, no automated diagnostic tools have been developed to identify multiple diseases using X-ray, Computed Tomography (CT) scan, or Magnetic Resonance Imaging (MRI) images. In this research, several Tate-of-the-art techniques have been applied to the Chest-Xray, CT scan, and MRI segmented images' datasets and trained them simultaneously. Deep learning models based on VGG16, VGG19, InceptionV3, ResNet50, Capsule Network, DenseNet architecture, Exception and Optimized Convolutional Neural Network (Optimized CNN) were applied to the detecting of Covid-19 contaminated situation, Alzheimer's disease, and Lung infected tissues. Due to efforts taken to reduce model losses and overfitting, the models' performances have improved in terms of accuracy. With the use of image augmentation techniques like flip-up, flip-down, flip-left, flip-right, etc., the size of the training dataset was further increased. In addition, we have proposed a mobile application by integrating a deep learning model to make the diagnosis faster. Eventually, we applied the Image fusion technique to analyze the medical images by extracting meaningful insights from the multimodal imaging modalities. © 2022 IEEE.

Computer Vision-Based IoT Architecture for Post COVID-19 Preventive Measures

The COVID-19 pandemic has wreaked havoc on people all across the world. Even though the number of verified COVID-19 cases is steadily decreasing, the danger persists. Only societal awareness and preventative measures can assist to minimize the number of impacted patients in the work environment. People often forget to wear masks before entering the work premises or are not careful enough to wear masks correctly. Keeping this in mind, this paper proposes an IoT-based architecture for taking all essential steps to combat the COVID-19 pandemic. The proposed low-cost architecture is divided into three components: one to detect face masks by using deep learning technologies, another to monitor contactless body temperature and the other to dispense disinfectants to the visitors. At first, we review all the existing state-of-the-art technologies, then we design and develop a working prototype. Here, we present our results with the accuracy of 97.43% using a deep Convolutional Neural Network (CNN) and 99.88% accuracy using MobileNetV2 deep learning architecture for automatic face mask detection. © 2023 by the authors.

X-Ray Image Classification for COVID-19 Using Transfer Learning

SARS-CoV-2, the cause of one of the significant pandemics in history, first appeared in Wuhan, China. It spreads rapidly, with symptoms like fever, cough, tiredness, and loss of taste or smell. We came up with many measures where the most effective was vaccines. Yet it's not enough against the rapidly appearing waves of SARS-CoV-2. A deep learning algorithm has proven efficient in detecting Covid-19 based on pneumonia and respiratory problems. These problems have been identified with the help of CT scans and X-ray images. It'll make it a lot easier to determine who's Infected and would save a lot of time and expenses overall would provide for extensive relief in the Covid-19 pandemic. This paper uses publically available COVID-19 X-Ray and CT Scan images to create a dataset. The Deep Learning based model is used to train and test the dataset. In the experiment, the overall accuracy is 98%, and in the testing process, the overall accuracy is 99%. © 2023 The authors and IOS Press.