GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest

Dental Caries diagnosis from bitewing images using convolutional neural networks

BACKGROUND

Dental caries, also known as tooth decay, is a widespread and long-standing condition that affects people of all ages. This ailment is caused by bacteria that attach themselves to teeth and break down sugars, creating acid that gradually wears away at the tooth structure. Tooth discoloration, pain, and sensitivity to hot or cold foods and drinks are common symptoms of tooth decay. Although this condition is prevalent among all age groups, it is especially prevalent in children with baby teeth. Early diagnosis of dental caries is critical to preventing further decay and avoiding costly tooth repairs. Currently, dentists employ a time-consuming and repetitive process of manually marking tooth lesions after conducting radiographic exams. However, with the rapid development of artificial intelligence in medical imaging research, there is a chance to improve the accuracy and efficiency of dental diagnosis.

METHODS

This study introduces a data-driven model for accurately diagnosing dental decay through the use of Bitewing radiology images using convolutional neural networks. The dataset utilized in this research includes 713 patient images obtained from the Samin Maxillofacial Radiology Center located in Tehran, Iran. The images were captured between June 2020 and January 2022 and underwent processing via four distinct Convolutional Neural Networks. The images were resized to 100 × 100 and then divided into two groups: 70% (4219) for training and 30% (1813) for testing. The four networks employed in this study were AlexNet, ResNet50, VGG16, and VGG19.

RESULTS

Among different well-known CNN architectures compared in this study, the VGG19 model was found to be the most accurate, with a 93.93% accuracy.

CONCLUSION

This promising result indicates the potential for developing an automatic AI-based dental caries diagnostic model from Bitewing images. It has the potential to serve patients or dentists as a mobile app or cloud-based diagnosis service (clinical decision support system).

How intra-source imbalanced datasets impact the performance of deep learning for COVID-19 diagnosis using chest X-ray images

Over the past decade, the use of deep learning has been widely increasing in the medical image diagnosis field. Deep learning-based methods' (DLMs) performance strongly relies on training data. Therefore, researchers often focus on collecting as much data as possible from different medical facilities or developing approaches to avoid the impact of inter-category imbalance (ICI), which means a difference in data quantity among categories. However, due to the ICI within each medical facility, medical data are often isolated and acquired in different settings among medical facilities, known as the issue of intra-source imbalance (ISI) characteristic. This imbalance also impacts the performance of DLMs but receives negligible attention. In this study, we study the impact of the ISI on DLMs by comparison of the version of a deep learning model that was trained separately by an intra-source imbalanced chest X-ray (CXR) dataset and an intra-source balanced CXR dataset for COVID-19 diagnosis. The finding is that using the intra-source imbalanced dataset causes a serious training bias, although the dataset has a good inter-category balance. In contrast, the deep learning model performed a reliable diagnosis when trained on the intra-source balanced dataset. Therefore, our study reports clear evidence that the intra-source balance is vital for training data to minimize the risk of poor performance of DLMs.

CSGNN: Contamination Warning and Control of Food Quality via Contrastive Self-Supervised Learning-Based Graph Neural Network

Effective contamination warning and control of food quality can significantly reduce the likelihood of food quality safety incidents. Existing food contamination warning models for food quality rely on supervised learning, do not model the complex feature associations between detection samples, and do not consider the unevenness of detection data categories. In this paper, To overcome these limitations, we propose a Contrastive Self-supervised learning-based Graph Neural Network framework (CSGNN) for contamination warning of food quality. Specifically, we structure the graph for detecting correlations between samples and then define the positive and negative instance pairs for contrastive learning based on attribute networks. Further, we use a self-supervised approach to capture the complex relationships between detection samples. Finally, we assessed each sample's contamination level based on the absolute value of the subtraction of the prediction scores from multiple rounds of positive and negative instances obtained by the CSGNN. Moreover, we conducted a sample study on a batch of dairy product detection data in a Chinese province. The experimental results show that CSGNN outperforms other baseline models in contamination assessment of food quality, with AUC and recall of unqualified samples reaching 0.9188 and 1.0000, respectively. Meanwhile, our framework provides interpretable contamination classification for food detection. This study provides an efficient early warning method with precise and hierarchical contamination classification for contamination warning of food quality work.

LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery

Automatic detection of lung diseases using AI-based tools became very much necessary to handle the huge number of cases occurring across the globe and support the doctors. This paper proposed a novel deep learning architecture named LWSNet (Light Weight Stacking Network) to separate Covid-19, cold pneumonia, and normal chest x-ray images. This framework is based on single, double, triple, and quadruple stack mechanisms to address the above-mentioned tri-class problem. In this framework, a truncated version of standard deep learning models and a lightweight CNN model was considered to conviniently deploy in resource-constraint devices. An evaluation was conducted on three publicly available datasets alongwith their combination. We received 97.28%, 96.50%, 97.41%, and 98.54% highest classification accuracies using quadruple stack. On further investigation, we found, using LWSNet, the average accuracy got improved from individual model to quadruple model by 2.31%, 2.55%, 2.88%, and 2.26% on four respective datasets.

Biased, wrong and counterfeited evidences published during the COVID-19 pandemic, a systematic review of retracted COVID-19 papers

In 2020 COVID-19 led to an unprecedented stream of papers being submitted to journals. Scientists and physicians all around the globe were in need for information about this new disease. In this climate, many articles were accepted after extremely fast peer-reviews to provide the scientific community with the latest discoveries and knowledge. Unfortunately, this also led to articles retraction due to authors' misconduct or errors in methodology and/or conclusions. The aim of this study is to investigate the number and characteristics of retracted papers, and to explore the main causes that led to retraction. We conducted a systematic review on retracted articles, using PubMed as data source. Our inclusion criteria were the following: English-language retracted articles that reported original data, results, opinions or hypotheses on COVID-19 and Sars-CoV-2. Twenty-seven retracted articles were identified, mainly reporting observational studies and opinion pieces. Many articles published during the first year of the pandemic have been retracted, mainly due to the authors' scientific misconduct. Duplications, plagiarism, frauds and absence of consent, were the main reasons for retractions. In modern medicine, researchers are required to publish frequently, and, especially during situations like the COVID-19 pandemic, when articles were rapidly published, gaps in peer-reviews system and in the path to scientific publication arose.

Combined Cloud-Based Inference System for the Classification of COVID-19 in CT-Scan and X-Ray Images

In the past few years, most of the work has been done around the classification of covid-19 using different images like CT-scan, X-ray, and ultrasound. But none of that is capable enough to deal with each of these image types on a single common platform and can identify the possibility that a person is suffering from COVID or not. Thus, we realized there should be a platform to identify COVID-19 in CT-scan and X-ray images on the fly. So, to fulfill this need, we proposed an AI model to identify CT-scan and X-ray images from each other and then use this inference to classify them of COVID positive or negative. The proposed model uses the inception architecture under the hood and trains on the open-source extended covid-19 dataset. The dataset consists of plenty of images for both image types and is of size 4 GB. We achieved an accuracy of 100%, average macro-Precision of 100%, average macro-Recall of 100%, average macro f1-score of 100%, and AUC score of 99.6%. Furthermore, in this work, cloud-based architecture is proposed to massively scale and load balance as the Number of user requests rises. As a result, it will deliver a service with minimal latency to all users.

Improved COVID-19 detection with chest x-ray images using deep learning

The novel coronavirus disease, which originated in Wuhan, developed into a severe public health problem worldwide. Immense stress in the society and health department was advanced due to the multiplying numbers of COVID carriers and deaths. This stress can be lowered by performing a high-speed diagnosis for the disease, which can be a crucial stride for opposing the deadly virus. A good large amount of time is consumed in the diagnosis. Some applications that use medical images like X-Rays or CT-Scans can pace up the time used in diagnosis. Hence, this paper aims to create a computer-aided-design system that will use the chest X-Ray as input and further classify it into one of the three classes, namely COVID-19, viral Pneumonia, and healthy. Since the COVID-19 positive chest X-Rays dataset was low, we have exploited four pre-trained deep neural networks (DNNs) to find the best for this system. The dataset consisted of 2905 images with 219 COVID-19 cases, 1341 healthy cases, and 1345 viral pneumonia cases. Out of these images, the models were evaluated on 30 images of each class for the testing, while the rest of them were used for training. It is observed that AlexNet attained an accuracy of 97.6% with an average precision, recall, and F1 score of 0.98, 0.97, and 0.98, respectively.

Review of COVID-19 testing and diagnostic methods

More than six billion tests for COVID-19 has been already performed in the world. The testing for SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2) virus and corresponding human antibodies is essential not only for diagnostics and treatment of the infection by medical institutions, but also as a pre-requisite for major semi-normal economic and social activities such as international flights, off line work and study in offices, access to malls, sport and social events. Accuracy, sensitivity, specificity, time to results and cost per test are essential parameters of those tests and even minimal improvement in any of them may have noticeable impact on life in the many countries of the world. We described, analyzed and compared methods of COVID-19 detection, while representing their parameters in 22 tables. Also, we compared test performance of some FDA approved test kits with clinical performance of some non-FDA approved methods just described in scientific literature. RT-PCR still remains a golden standard in detection of the virus, but a pressing need for alternative less expensive, more rapid, point of care methods is evident. Those methods that may eventually get developed to satisfy this need are explained, discussed, quantitatively compared. The review has a bioanalytical chemistry prospective, but it may be interesting for a broader circle of readers who are interested in understanding and improvement of COVID-19 testing, helping eventually to leave COVID-19 pandemic in the past.

Deep Learning and Medical Image Analysis for COVID-19 Diagnosis and Prediction

The coronavirus disease 2019 (COVID-19) pandemic has imposed dramatic challenges to health-care organizations worldwide. To combat the global crisis, the use of thoracic imaging has played a major role in diagnosis, prediction, and management for COVID-19 patients with moderate to severe symptoms or with evidence of worsening respiratory status. In response, the medical image analysis community acted quickly to develop and disseminate deep learning models and tools to meet the urgent need of managing and interpreting large amounts of COVID-19 imaging data. This review aims to not only summarize existing deep learning and medical image analysis methods but also offer in-depth discussions and recommendations for future investigations. We believe that the wide availability of high-quality, curated, and benchmarked COVID-19 imaging data sets offers the great promise of a transformative test bed to develop, validate, and disseminate novel deep learning methods in the frontiers of data science and artificial intelligence. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 24 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Estimating the state of epidemics spreading with graph neural networks

When an epidemic spreads into a population, it is often impractical or impossible to continuously monitor all subjects involved. As an alternative, we propose using algorithmic solutions that can infer the state of the whole population from a limited number of measures. We analyze the capability of deep neural networks to solve this challenging task. We base our proposed architecture on Graph Convolutional Neural Networks. As such, it can reason on the effect of the underlying social network structure, which is recognized as the main component in spreading an epidemic. The proposed architecture can reconstruct the entire state with accuracy above 70%, as proven by two scenarios modeled on the CoVid-19 pandemic. The first is a generic homogeneous population, and the second is a toy model of the Boston metropolitan area. Note that no retraining of the architecture is necessary when changing the model.

Internet of Medical Things-Based COVID-19 Detection in CT Images Fused with Fuzzy Ensemble and Transfer Learning Models

One of the most difficult research areas in today's healthcare industry to combat the coronavirus pandemic is accurate COVID-19 detection. Because of its low infection miss rate and high sensitivity, chest computed tomography (CT) imaging has been recommended as a viable technique for COVID-19 diagnosis in a number of recent clinical investigations. This article presents an Internet of Medical Things (IoMT)-based platform for improving and speeding up COVID-19 identification. Clinical devices are connected to network resources in the suggested IoMT platform using cloud computing. The method enables patients and healthcare experts to work together in real time to diagnose and treat COVID-19, potentially saving time and effort for both patients and physicians. In this paper, we introduce a technique for classifying chest CT scan images into COVID, pneumonia, and normal classes that use a Sugeno fuzzy integral ensemble across three transfer learning models, namely SqueezeNet, DenseNet-201, and MobileNetV2. The suggested fuzzy ensemble techniques outperform each individual transfer learning methodology as well as trainable ensemble strategies in terms of accuracy. The suggested MobileNetV2 fused with Sugeno fuzzy integral ensemble model has a 99.15% accuracy rate. In the present research, this framework was utilized to identify COVID-19, but it may also be implemented and used for medical imaging analyses of other disorders.

Role of Artificial Intelligence in COVID-19 Detection

The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.

A Secure and reliable RFID authentication protocol using digital schnorr cryptosystem for IoT-enabled healthcare in COVID-19 scenario

The Internet of Things (IoT) plays an important role in various healthcare applications. Radio Frequency IDentification (RFID) is widely used as a leading identification technology in a variety of IoT-health applications. In 2020, the number of cases of novel Corona Virus Disease 2019 ( n COVID-19) was increased rapidly throughout the world. Herein, IoT-Health enables the more convenient ways to access remotely and efficiently the medical services for the patients, also provides health monitoring by the doctors, physicians, and nurses over the Internet. However, security and privacy are considered key concerns in RFID-based IoT-health systems due to wireless communication over the channel. There could be huge risks of leakage of the patient's sensitive information, medical data, privacy of the patients, and so forth. To overcome these shortcomings, we have put forward a secure and reliable RFID authentication protocol using Digital Schnorr Cryptosystem for IoT-Health in COVID-19 patients care named S R 2 AP-DSC. Compared with the similar existing protocols, the security analysis followed by the performance evaluation of our proposed protocol demonstrates the minimal computation overheads and also provides resistance to various well-known security attacks. The AVISPA and Scyther simulation results confirm that the proposed protocol is safe under active and passive attacks. The overall analysis shows that the S R 2 AP-DSC is relatively superior to the other similar existing protocols.

A Histogram-Based Low-Complexity Approach for the Effective Detection of COVID-19 Disease from CT and X-ray Images

The global COVID-19 pandemic certainly has posed one of the more difficult challenges for researchers in the current century. The development of an automatic diagnostic tool, able to detect the disease in its early stage, could undoubtedly offer a great advantage to the battle against the pandemic. In this regard, most of the research efforts have been focused on the application of Deep Learning (DL) techniques to chest images, including traditional chest X-rays (CXRs) and Computed Tomography (CT) scans. Although these approaches have demonstrated their effectiveness in detecting the COVID-19 disease, they are of huge computational complexity and require large datasets for training. In addition, there may not exist a large amount of COVID-19 CXRs and CT scans available to researchers. To this end, in this paper, we propose an approach based on the evaluation of the histogram from a common class of images that is considered as the target. A suitable inter-histogram distance measures how this target histogram is far from the histogram evaluated on a test image: if this distance is greater than a threshold, the test image is labeled as anomaly, i.e., the scan belongs to a patient affected by COVID-19 disease. Extensive experimental results and comparisons with some benchmark state-of-the-art methods support the effectiveness of the developed approach, as well as demonstrate that, at least when the images of the considered datasets are homogeneous enough (i.e., a few outliers are present), it is not really needed to resort to complex-to-implement DL techniques, in order to attain an effective detection of the COVID-19 disease. Despite the simplicity of the proposed approach, all the considered metrics (i.e., accuracy, precision, recall, and F-measure) attain a value of 1.0 under the selected datasets, a result comparable to the corresponding state-of-the-art DNN approaches, but with a remarkable computational simplicity.

Analytical and qualitative investigation of COVID-19 mathematical model under fractional differential operator

In the current article, we aim to study in detail a novel coronavirus (2019-nCoV or COVID-19) mathematical model for different aspects under Caputo fractional derivative. First, from analysis point of view, existence is necessary to be investigated for any applied problem. Therefore, we used fixed point theorem's due to Banach's and Schaefer's to establish some sufficient results regarding existence and uniqueness of the solution to the proposed model. On the other hand, stability is important in respect of approximate solution, so we have developed condition sufficient for the stability of Ulam-Hyers and their different types for the considered system. In addition, the model has also been considered for semianalytical solution via Laplace Adomian decomposition method (LADM). On Matlab, by taking some real data about Pakistan, we graph the obtained results. In the last of the manuscript, a detail discussion and brief conclusion are provided.

Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio

The novel 2019 coronavirus disease (COVID-19) has infected over 141 million people worldwide since April 20, 2021. More than 200 countries around the world have been affected by the coronavirus pandemic. Screening for COVID-19, we use fast and inexpensive images from computed tomography (CT) scans. In this paper, ResNet-50, VGG-16, convolutional neural network (CNN), convolutional auto-encoder neural network (CAENN), and machine learning (ML) methods are proposed for classifying Chest CT Images of COVID-19. The dataset consists of 1252 CT scans that are positive and 1230 CT scans that are negative for COVID-19 virus. The proposed models have priority over the other models that there is no need of pre-trained networks and data augmentation for them. The classification accuracies of ResNet-50, VGG-16, CNN, and CAENN were obtained 92.24%, 94.07%, 93.84%, and 93.04% respectively. Among ML classifiers, the nearest neighbor (NN) had the highest performance with an accuracy of 94%.

Choquet fuzzy integral-based classifier ensemble technique for COVID-19 detection

The COVID-19 outbreak has resulted in a global pandemic and led to more than a million deaths to date. COVID-19 early detection is essential for its mitigation by controlling its spread from infected patients in communities through quarantine. Although vaccination has started, it will take time to reach everyone, especially in developing nations, and computer scientists are striving to come up with competent methods using image analysis. In this work, a classifier ensemble technique is proposed, utilizing Choquet fuzzy integral, wherein convolutional neural network (CNN) based models are used as base classifiers. It classifies chest X-ray images from patients with common Pneumonia, confirmed COVID-19, and healthy lungs. Since there are few samples of COVID-19 cases for training on a standard CNN model from scratch, we use the transfer learning scheme to train the base classifiers, which are InceptionV3, DenseNet121, and VGG19. We utilize the pre-trained CNN models to extract features and classify the chest X-ray images using two dense layers and one softmax layer. After that, we combine the prediction scores of the data from individual models using Choquet fuzzy integral to get the final predicted labels, which is more accurate than the prediction by the individual models. To determine the fuzzy-membership values of each classifier for the application of Choquet fuzzy integral, we use the validation accuracy of each classifier. The proposed method is evaluated on chest X-ray images in publicly available repositories (IEEE and Kaggle datasets). It provides 99.00%, 99.00%, 99.00%, and 99.02% average recall, precision, F-score, and accuracy, respectively. We have also evaluated the performance of the proposed model on an inter-dataset experimental setup, where chest X-ray images from another dataset (CMSC-678-ML-Project GitHub dataset) are fed to our trained model and we have achieved 99.05% test accuracy on this dataset. The results are better than commonly used classifier ensemble methods as well as many state-of-the-art methods.

Choquet fuzzy integral-based classifier ensemble technique for COVID-19 detection

The COVID-19 outbreak has resulted in a global pandemic and led to more than a million deaths to date. COVID-19 early detection is essential for its mitigation by controlling its spread from infected patients in communities through quarantine. Although vaccination has started, it will take time to reach everyone, especially in developing nations, and computer scientists are striving to come up with competent methods using image analysis. In this work, a classifier ensemble technique is proposed, utilizing Choquet fuzzy integral, wherein convolutional neural network (CNN) based models are used as base classifiers. It classifies chest X-ray images from patients with common Pneumonia, confirmed COVID-19, and healthy lungs. Since there are few samples of COVID-19 cases for training on a standard CNN model from scratch, we use the transfer learning scheme to train the base classifiers, which are InceptionV3, DenseNet121, and VGG19. We utilize the pre-trained CNN models to extract features and classify the chest X-ray images using two dense layers and one softmax layer. After that, we combine the prediction scores of the data from individual models using Choquet fuzzy integral to get the final predicted labels, which is more accurate than the prediction by the individual models. To determine the fuzzy-membership values of each classifier for the application of Choquet fuzzy integral, we use the validation accuracy of each classifier. The proposed method is evaluated on chest X-ray images in publicly available repositories (IEEE and Kaggle datasets). It provides 99.00%, 99.00%, 99.00%, and 99.02% average recall, precision, F-score, and accuracy, respectively. We have also evaluated the performance of the proposed model on an inter-dataset experimental setup, where chest X-ray images from another dataset (CMSC-678-ML-Project GitHub dataset) are fed to our trained model and we have achieved 99.05% test accuracy on this dataset. The results are better than commonly used classifier ensemble methods as well as many state-of-the-art methods.

Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach

COVID-19 is a disease caused by the SARS-CoV-2 virus. The COVID-19 virus spreads when a person comes into contact with an affected individual. This is mainly through drops of saliva or nasal discharge. Most of the affected people have mild symptoms while some people develop acute respiratory distress syndrome (ARDS), which damages organs like the lungs and heart. Chest X-rays (CXRs) have been widely used to identify abnormalities that help in detecting the COVID-19 virus. They have also been used as an initial screening procedure for individuals highly suspected of being infected. However, the availability of radiographic CXRs is still scarce. This can limit the performance of deep learning (DL) based approaches for COVID-19 detection. To overcome these limitations, in this work, we developed an Auxiliary Classifier Generative Adversarial Network (ACGAN), to generate CXRs. Each generated X-ray belongs to one of the two classes COVID-19 positive or normal. To ensure the goodness of the synthetic images, we performed some experimentation on the obtained images using the latest Convolutional Neural Networks (CNNs) to detect COVID-19 in the CXRs. We fine-tuned the models and achieved more than 98% accuracy. After that, we also performed feature selection using the Harmony Search (HS) algorithm, which reduces the number of features while retaining classification accuracy. We further release a GAN-generated dataset consisting of 500 COVID-19 radiographic images.