A Dynamic-Static Dual Input Deep Neural Network Algorithm for Diagnosing COVID-19 by Cough

The COVID-19 (corona virus disease 2019) has caused serious impacts worldwide. Many scholars have done a lot of research on the prevention and control of the epidemic. The diagnosis of COVID-19 by cough is non-contact, low-cost, and easy-access, however, such research is still relatively scarce in China. Mel frequency cepstral coefficients (MFCC) feature can only represent the static sound feature, while the first-order differential MFCC feature can also reflect the dynamic feature of sound. In order to better prevent and treat COVID-19, the paper proposes a dynamic-static dual input deep neural network algorithm for diagnosing COVID-19 by cough. Based on Coswara dataset, cough audio is clipped, MFCC and first-order differential MFCC features are extracted, and a dynamic and static feature dual-input neural network model is trained. The model adopts a statistic pooling layer so that different length of MFCC features can be input. The experiment results show the proposed algorithm can significantly improve the recognition accuracy, recall rate, specificity, and F1-score compared with the existing models. © 2023 Chinese Institute of Electronics. All rights reserved.

Exploring Social Media for Early Detection of Depression in COVID-19 Patients

The COVID-19 pandemic has caused substantial damage to global health. Even though three years have passed, the world continues to struggle with the virus. Concerns are growing about the impact of COVID-19 on the mental health of infected individuals, who are more likely to experience depression, which can have long-lasting consequences for both the affected individuals and the world. Detection and intervention at an early stage can reduce the risk of depression in COVID-19 patients. In this paper, we investigated the relationship between COVID-19 infection and depression through social media analysis. Firstly, we managed a dataset of COVID-19 patients that contains information about their social media activity both before and after infection. Secondly, We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression. Thirdly, we proposed a deep neural network for early prediction of depression risk. This model considers daily mood swings as a psychiatric signal and incorporates textual and emotional characteristics via knowledge distillation. Experimental results demonstrate that our proposed framework outperforms baselines in detecting depression risk, with an AUROC of 0.9317 and an AUPRC of 0.8116. Our model has the potential to enable public health organizations to initiate prompt intervention with high-risk patients. © 2023 ACM.

Automatic Detection of Lung Ultrasound Artifacts using a Deep Neural Networks approach

The COVID-19 pandemic has challenged many of the healthcare systems around the world. Many patients who have been hospitalized due to this disease develop lung damage. In low and middle-income countries, people living in rural and remote areas have very limited access to adequate health care. Ultrasound is a safe, portable and accessible alternative;however, it has limitations such as being operator-dependent and requiring a trained professional. The use of lung ultrasound volume sweep imaging is a potential solution for this lack of physicians. In order to support this protocol, image processing together with machine learning is a potential methodology for an automatic lung damage screening system. In this paper we present an automatic detection of lung ultrasound artifacts using a Deep Neural Network, identifying clinical relevant artifacts such as pleural and A-lines contained in the ultrasound examination taken as part of the clinical screening in patients with suspected lung damage. The model achieved encouraging preliminary results such as sensitivity of 94%, specificity of 81%, and accuracy of 89% to identify the presence of A-lines. Finally, the present study could result in an alternative solution for an operator-independent lung damage screening in rural areas, leading to the integration of AI-based technology as a complementary tool for healthcare professionals. © 2023 SPIE.

Deep-learning models for Covid-19 Detection Using Chest X-Ray Images

Deep Convolutional Neural Networks are a form of neural network that can categorize, recognize, or separate images. The problem of COVID-19 detection has become the world's most complex challenge since 2019. In this research work, Chest X-Ray images are used to detect patients' Covid Positive or Negative with the help of pre-trained models: VGG16, InceptionV3, ResNet50, and InceptionResNetV2. In this paper, 821 samples are used for training, 186 samples for validation, and 184 samples are used for testing. Hybrid model InceptionResNetV2 has achieved overall maximum accuracy of 94.56% with a Recall value of 96% for normal CXR images, and a precision of 95.12% for Covid Positive images. The lowest accuracy was achieved by the ResNet50 model of 92.93% on the testing dataset, and a Recall of 93.93% was achieved for the normal images. Throughout the implementation process, it was discovered that factors like epoch had a considerable impact on the model's accuracy. Consequently, it is advised that the model be trained with a sufficient number of epochs to provide reliable classification results. The study's findings suggest that deep learning models have an excellent potential for correctly identifying the covid positive or covid negative using CXR images. © 2023 IEEE.

Time series prediction of the COVID-19 outbreak in India using LSTM based deep learning models

The epidemic caused by COVID-19 presents a significant risk to the continuation of human civilisation and has already done irreparable damage to society. In this paper, forecasting of Coronavirus outbreak in India is performed by LSTM and CovnLSTM deep neural network techniques. COVID-19 data of confirmed cases of India is used. It was taken from John Hopkins University. The loss rate of ConvLSTM is lower than LSTM and RMSE of ConvLSTM is lower than LSTM. For training Covn-LSTM shows 0.069% and testing ConvLSTM shows 0.32% improvement over LSTM model. Therefore, ConvLSTM outperformed over LSTM model. Further wise selection of hyper-parameters could increase the accuracy of the models. © 2023 IEEE.

CovidXNet: An Ensemble of Deep Neural Networks for Non-invasive Binary Identification of COVID-19 through Chest Radiographs

This study compared five different image classification algorithms, namely VGG16, VGG19, AlexNet, DenseNet, and ConVNext, based on their ability to detect and classify COVID-19-related cases given chest X-ray images. Using performance metrics like accuracy, F1 score, precision, recall, and MCC compared these intelligent classification algorithms. Upon testing these algorithms, the accuracy for each model was quite unsatisfactory, ranging from 80.00% to 92.50%, provided it is for medical application. As such, an ensemble learning-based image classification model, made up of AlexNet and VGG19 called CovidXNet, was proposed to detect COVID-19 through chest X-ray images discriminating between health and pneumonic lung images. CovidXNet achieved an accuracy of 97.00%, which was significantly better considering past results. Further studies may be conducted to increase the accuracy, particularly for identifying and classifying chest radiographs for COVID-19-related cases, since the current model may still provide false negatives, which may be detrimental to the prevention of the spread of the virus. © 2022 IEEE.

U-Net based Semantic Segmentation for Touchless Fingerprint Technology: A Reflective Review

Touch-based fingerprints are widely used in today's world;even with all the success, the touch-based nature of these is a threat, especially in this COVID-19 period. A solution to the same is the introduction of Touchless Fingerprint Technology. The workflow of a touchless system varies vastly from its touch-based counterpart in terms of acquisition, pre-processing, image enhancement, and fingerprint verification. One significant difference is the methods used to segment desired fingerprint regions. This literature focuses on pixel-level classification or semantic segmentation using U-Net, a key yet challenging task. A plethora of semantic segmentation methods have been applied in this field. In this literature, a spectrum of efforts in the field of semantic segmentation using U-Net is investigated along with the components that are integral while training and testing a model, like optimizers, loss functions, and metrics used for evaluation and enumeration of results obtained. © 2022 IEEE.

Diagnosis of COVID-19 from the X-Ray images using BAT Algorithm with Deep Convolutional Neural Network

The COVID-19 widespread has posed a chief contest to the scientific community around the world. For patients with COVID-19 illness, the international community is working to uncover, implement, or invent new approaches for diagnosis and action. A opposite transcription-polymerase chain reaction is currently a reliable tactic for diagnosing infected people. This is a time- and money-consuming procedure. Consequently, the development of new methods is critical. Using X-ray images of the lungs, this research article developed three stages for detecting and diagnosing COVID-19 patients. The median filtering is used to remove the unwanted noised during pre-processing stage. Then, Otsu thresholding technique is used for segmenting the affected regions, where Spider Monkey Optimization (SMO) is used to select the optimal threshold. Finally, the optimized Deep Convolutional Neural Network (DCNN) is used for final classification. The benchmark COVID dataset and balanced COVIDcxr dataset are used to test projected model's performance in this study. Classification of the results shows that the optimized DCNN architecture outperforms the other pre-trained techniques with an accuracy of 95.69% and a specificity of 96.24% and sensitivity of 94.76%. To identify infected lung tissue in images, here SMO-Otsu thresholding technique is used during the segmentation stage and achieved 95.60% of sensitivity and 95.8% of specificity. © 2023 IEEE.

Covid19 Disease Assessment Using CNN Architecture

Recently, the COVID-19 pandemic has emerged as one of the world's most critical public health concerns. One of the biggest problems in the present COVID-19 outbreak is the difficulty of accurately separating COVID-19 cases from non-COVID-19 cases at an affordable price and in the initial stages. Besides the use of antigen Rapid Test Kit (RTK) and Reverse Transcription Polymerase Chain Reaction (RT-PCR), chest x-rays (CXR) can also be used to identify COVID-19 patients. Unfortunately, manual checks may produce inaccurate results, delay treatment or even be fatal. Because of differences in perception and experience, the manual method can be chaotic and imprecise. Technology has progressed to the point where we can solve this problem by training a Deep Learning (DL) model to distinguish the normal and COVID-19 X-rays. In this work, we choose the Convolutional Neural Network (CNN) as our DL model and train it using Kaggle datasets that include both COVID-19 and normal CXR data. The developed CNN model is then deployed on the website after going through a training and validation process. The website layout is straightforward to navigate. A CXR can be uploaded and a prediction made with minimal effort from the patient. The website assists in determining whether they have been exposed to COVID-19 or not. © 2023 IEEE.

Towards Lifting the Trade-off between Accuracy and Adversarial Robustness of Deep Neural Networks with Application on COVID 19 CT Image Classification and Medical Image Segmentation

Deep neural networks (DNNs) are vulnerable to adversarial noises. Adversarial training is a general strategy to improve DNN robustness. But training a DNN model with adversarial noises may result in a much lower accuracy on clean data, which is termed the trade-off between accuracy and adversarial robustness. Towards lifting this trade-off, we propose an adversarial training method that generates optimal adversarial training samples. We evaluate our methods on PathMNIST and COVID-19 CT image classification tasks, where the DNN model is ResNet-18, and Heart MRI and Prostate MRI image segmentation tasks, where the DNN model is nnUnet. All these four datasets are publicly available. The experiment results show that our method has the best robustness against adversarial noises and has the least accuracy degradation compared to the other defense methods. © 2023 SPIE.

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.

Comparative study of Deep Learning Models for Covid 19 Diagnosis

The Covid 19 pandemic that started a couple of years ago has had a devastating effect on mankind across the globe. The disease had no known treatment. Early detection and prevention was very important to curtail the effects of the Pandemic. In this work two deep learning models the RestNet and the models are proposed for diagnosing Corona from chest X-rays and CT scans. The models were trained with publicly available data sets of covid and non covid images. It has been found that Inception V3 performs better than ResNet for chest x-rays and RestNet performs better for CT Scans. The performance of the RestNet is found to be similar for both the chest x-rays and CT scans datasets. © 2023 IEEE.

Transfer Learning Model in Deep Neural Network for COVID-19 Prediction from CT Images

Deep learning models are often used to process radi-ological images automatically and can accurately train networks' weights on appropriate datasets. One of the significant benefits of the network is that it is possible to use the weight of a pre-trained network for other applications by fine-tuning the current weight. The primary purpose of this work is to employ a pre-trained deep neural framework known as transfer learning to detect and diagnose COVID-19 in CT images automatically. This paper uses a popular deep neural model, ResNet152, as a neural transfer approach. The presented framework uses the weight obtained from the ImageNet dataset, fine-tuned by the dataset used in the work. The effectiveness of the suggested COVID-19 prediction system is evaluated experimentally and compared with DenseNet, another transfer learning model. The recommended ResNet152 transfer learning model exhibits improved performance and has a 99% accuracy when analogized with the DenseNet201 transfer learning model. © 2022 IEEE.

Masked Face Recognition Using Multi-Task Cascaded Convolutional Networks and FaceNet

Face masks have been widely used since the start of the COVID-19 pandemic. Facial detection and recognition technologies, such as the iPhone's Face ID, heavily rely on seeing the facial features that are now obscured due to wearing a face mask. Currently, the only way to utilize Face ID with a mask on is by having an Apple Watch as well. As such, this paper intends to find initial means of a reliable personal facial recognition system while the user is wearing a face mask without having the need for an Apple Watch. This may also be applicable to other security systems or measures. Through the use of Multi-Task Cascaded Convolutional Networks or MTCNN, a type of neural network which identifies faces and facial landmarks, and FaceNet, a deep neural network utilized for deriving features from a picture of a face, the masked face of the user could be identified and more importantly be recognized. Utilizing MTCNN, detecting the masked faces and automatically cropping them from the raw images are done. The learning phase then takes place wherein the exposed facial features are given emphasis while the masks themselves are excluded as a factor in recognition. Data in the form of images are acquired from taking multiple pictures of a certain individual's face as well as from repositories online for other people's faces. Images used are taken in various settings or modes such as different lighting levels, facial angles, head angles, colors and designs of face masks, and the presence or absence of glasses. The goal is to recognize whether it is the certain individual or not in the image. The training accuracy is 99.966% while the test accuracy is 99.921%. © 2022 IEEE.

Ischemic Heart Disease Prediction Using Machine Learning and Deep Learning Techniques

To have control over heart patient health, we need a capable detector which finds out based onhealth records. The idea is to work on coronary artery disease (CAD), which has been the majorhealth issue at present. We took a data set to train our system (machine learning algorithm) towork on the CAD and identify the user's health stage and provide the required information. Asper previous analysis, we got accuracy of 96% now with a minor modification we are trying to impact the accuracy. CAD has been the major health disease that is leading to death in world at present after COVID19, it is causing 33% of death rate by a survey by WHO. So, it is essentialto overcome the disease with proper analysis and prevention, which is all about our project. We are trying to make healthcare handy such that a person that analyze and know about his/her health condition from anywhere and at any time regardless of working hours. © 2023 IEEE.

Deep Convolutional Neural Network Based Automatic COVID-19 Detection from Chest X-ray Images

To minimize the rate of death from COVID-19 and stop the disease from spreading early detection is vital. The normal RT-PCR tests for COVID-19 detection take a long time to complete. In contrast to this test, Covid-19 can be quickly detected using various machine-learning technologies. Previous studies only had access to smaller datasets, as COVID-19 data was not readily available back then. Since COVID-19 is a dangerous virus, the model needs to be robust and trustworthy, and the model must be trained on a large and diverse dataset. To overcome that problem, this study combines six publicly available Chest X-ray datasets to produce a larger and more diverse balanced dataset with a total of 68,424 images. In this study, we develop a CNN model that primarily entails two steps: (a) feature extraction and (b) classification, which are used to identify COVID-19 positive cases from X-ray images. The accuracy of this proposed model is 97.58%, which is higher than most state-of-the-art models. © 2022 IEEE.

A Temperature-Modified Dynamic Embedded Topic Model

Topic models are natural language processing models that can parse large collections of documents and automatically discover their main topics. However, conventional topic models fail to capture how such topics change as the collections evolve. To amend this, various researchers have proposed dynamic versions which are able to extract sequences of topics from timestamped document collections. Moreover, a recently-proposed model, the dynamic embedded topic model (DETM), joins such a dynamic analysis with the representational power of word and topic embeddings. In this paper, we propose modifying its word probabilities with a temperature parameter that controls the smoothness/sharpness trade-off of the distributions in an attempt to increase the coherence of the extracted topics. Experimental results over a selection of the COVID-19 Open Research Dataset (CORD-19), the United Nations General Debate Corpus, and the ACL Title and dataset show that the proposed model - nicknamed DETM-tau after the temperature parameter - has been able to improve the model's perplexity and topic coherence for all datasets.

Mask Detection and Classification in Thermal Face Images

Face masks are recommended to reduce the transmission of many viruses, especially SARS-CoV-2. Therefore, the automatic detection of whether there is a mask on the face, what type of mask is worn, and how it is worn is an important research topic. In this work, the use of thermal imaging was considered to analyze the possibility of detecting (localizing) a mask on the face, as well as to check whether it is possible to classify the type of mask on the face. The previously proposed dataset of thermal images was extended and annotated with the description of a type of mask and a location of a mask within a face. Different deep learning models were adapted. The best model for face mask detection turned out to be the Yolov5 model in the "nano" version, reaching mAP higher than 97% and precision of about 95%. High accuracy was also obtained for mask type classification. The best results were obtained for the convolutional neural network model built on an autoencoder initially trained in the thermal image reconstruction problem. The pretrained encoder was used to train a classifier which achieved an accuracy of 91%.

An Automated CHNN Model for the Classification and Detection of Lung Diseases using Transfer Learning

The COVID-19 pandemic began in December2019 and caused a global crisis. The WHO declared it a pandemic on March 11, 2020. Since October 10, 2020, COVID-19 has affected 200+ countries, causing over 37 million confirmed cases and 1 million deaths. RT-PCR is the usual method for detecting it, but it has drawbacks. Individuals who exhibit symptoms of COVID-19 but receive negative results from RT-PCR tests may be diagnosed with the disease using chest X-rays and CT scans, as these imaging techniques are capable of detecting lung abnormalities that are commonly associated with COVID-19, including consolidation and ground-glass opacities. The detection of COVID-19 systems faces numerous challenges, including false negatives, limited testing capacity, a scarcity of imaging equipment, and a shortage of data. With the increasing number of cases, there is a pressing need for a quicker, more cost-effective screening method. Chest X-ray scans can serve as a supplementary or confirming approach as they are fast and readily available. An Automated Hybrid Convolutional Neural network-Hopfield Neural Network (CHNN) is proposed in this study by extracting the features using VGG-19 for the classification and detection of lung diseases. In this work, both two-fold and multi-class classifications have been done with 99% and 97% accuracy respectively. © 2023 IEEE.

Analysis of the Impacts of COVID-19 using Deep Convolutional Neural Network

COVID-19 is the transmittable disease that emerged as a recent epidemic and threatened the lives of various people. The emerged pandemic initiated a change in the people's routine and impacted a serious financial crisis. This initiated a necessity for developing a deeper insight of the COVID-19 disease and multiple researches are performed based on the COVID-19 epidemic, which possess the challenges of basic analysis of information about the disease, lack of data, lack of knowledge about the parameters that cause disease and to overcome this a deep COVID-19 analysis epidemic via the deep CNN classifier is accomplished in the research. The impact of the disease is examined based on the gender, age group, symptoms and outbreak of the disease. This analysis provides comprehensive information about the disease and helps in making the preventive measures, which will greatly reduce the impacts of the disease. The accomplishment of deep CNN instinctively analyzes the essential features needed for the classification that helps in reducing the effort and time of the individuals. The performance is analyzed with the metrics specificity, accuracy and sensitivity, which obtained values of 0.48 %, 0.27 %, 2.82 % corresponding to and 2.88 %, 1.5 %, 0.36% considering training percentage, which is more efficient. © 2023 IEEE.