A CNN Transfer Learning -Electrocardiogram (ECG) Signal Approach to Predict COVID-19

Deep learning is increasingly gaining traction in cutting-edge medical sciences such as image classification, and genomics due to the high computational performance and accuracy in evaluating medical data. In this study, we investigate the cardiac properties of ECG Images and predict COVID-19 in a binary classification of patients who tested positive for COVID-19 and Normal Persons who tested negative. We analyzed the electrocardiogram (ECG) images by preprocessing the ECG data and building an ECG- Deep Learning- COVID-19 (ECG-DL-COVID) classifier to predict disease. The deep learning models in our experiments constituted CNN, Multi-Layer Perceptron (MLP), and Transfer Learning. Performance evaluation was done to compare the effectiveness of the proposed methodologies with other COVID-19 deep learning-related works. In the three experiments, we achieved an 87% prediction accuracy for MLP, a 90% prediction for CNN and a 93.8% prediction for Transfer Learning. Experimental results and performance evaluation show that the proposed models outperformed previous deep-learning models in the prediction of COVID-19 by a considerable margin. © 2023 IEEE.

COVID-19 prediction with Chest X-Ray images using CNN

Coronavirus has outbreak as an epidemic disease, created a pandemic situation for the public health across the Globe. Screening for the large masses is extremely crucial to control disease for the people in a neighborhood. Real-time-PCR[18] is the general diagnostic approach for pathological examination. However, the increasing figure of false results from the test has created a way in choosing alternative procedures. COVID-19 patient's X-rays images of chest has emerged as a significant approach for screening the COVID-19 disease. However, accuracy depends on the knowledge of a radiologist. X-Ray images of lungs may be proper assistive tool for diagnosis in reducing the burden of the doctor. Deep Learning techniques, especially Convolutional Neural Networks (CNN), have been shown to be effective for classification of images in the medical field. Diagnosing the COVID-19 using the four types of Deep-CNN models because they have pre-trained weights. Model needs to pre-trained on the ImageNet database in simplifying the large datasets. CNN-based architectures were found to be ideal in diagnosing the COVID-19 disease. The model having an efficiency of 0.9835 in accuracy, precision of 0.915, sensitivity of 0.963, specificity with 0.972, 0.987 F1 Score and 0.925 ROC AUC. © 2023 IEEE.

A Comprehensive Alert System Based on Social Distancing for Cautioning People Amidst the COVID-19 Pandemic Using Deep Neural Network Models

The World Health Organization (WHO) has suggested a successful social distancing strategy for reducing the COVID-19 virus spread in public places. All governments and national health bodies have mandated a 2-m physical distance between malls, schools, and congested areas. The existing algorithms proposed and developed for object detection are Simple Online and Real-time Tracking (SORT) and Convolutional Neural Networks (CNN). The YOLOv3 algorithm is used because YOLOv3 is an efficient and powerful real-time object detection algorithm in comparison with several other object detection algorithms. Video surveillance cameras are being used to implement this system. A model will be trained against the most comprehensive datasets, such as the COCO datasets, for this purpose. As a result, high-risk zones, or areas where virus spread is most likely, are identified. This may support authorities in enhancing the setup of a public space according to the precautionary measures to reduce hazardous zones. The developed framework is a comprehensive and precise solution for object detection that can be used in a variety of fields such as autonomous vehicles and human action recognition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Student Attention Base Facial Emotion State Recognition Using Convolutional Neural Network

E-learning system advancements give students new opportunities to better their academic performance and access e-learning education. Because it provides benefits over traditional learning, e-learning is becoming more popular. The coronavirus disease pandemic situation has caused educational institution cancelations all across the world. Around all over the world, more than a billion students are not attending educational institutions. As a result, learning criteria have taken on significant growth in e-learning, such as online and digital platform-based instruction. This study focuses on this issue and provides learners with a facial emotion recognition model. The CNN model is trained to assess images and detect facial expressions. This research is working on an approach that can see real-time facial emotions by demonstrating students' expressions. The phases of our technique are face detection using Haar cascades and emotion identification using CNN with classification on the FER 2013 datasets with seven different emotions. This research is showing real-time facial expression recognition and help teachers adapt their presentations to their student's emotional state. As a result, this research detects that emotions' mood achieves 62% accuracy, higher than the state-of-the-art accuracy while requiring less processing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

ECG-CCNet: Cardiovascular(Cardiac) and COVID19 Disease Classification Using Deep Convolutional Neural Network Learning Pipeline Approaches from Electrocardiography(ECG)- A Study

Experimental studies demonstrate that COVID-19 illness affects the cardiovascular as well as the pulmonary / lung tract. The limits of existing COVID-19 diagnostic procedures have been revealed. In contrast, to present diagnoses, such as low-sensitivity conventional RT-PCR testing and costly healthcare scanning equipment, implementing additional approaches for COVID-19 illness assessment would be advantageous for COVID-19 epidemic management. Furthermore, problems generated by COVID-19 on the cardiovascular tract must be detected rapidly and precisely using ECG. Considering the numerous advantages of electrocardiogram (ECG) functionalities, the proposed study offers a novel pipeline termed ECG-CCNet for examining the feasibility of employing ECG pulses to diagnose COVID-19. This study is a two-phase transfer learning (TL) approach is suggested for the prognosis of COVID-19 disorder, which includes feature mining utilizing DCNNs models and ensemble pipelining using ECG tracing imageries generated from ECG signals of COVID-19 diseased sufferers relying on the anomalies induced by COVID-19 pathogen on cardiovascular structures. A complete classification performance of 93.5% accuracy, 87% recall, 87.03% F1-score, 95.66% specificity, 87.16% precision, and 95.33% AUC attained by abnormal heartbeats, COVID-19, myocardial, and normal/healthy classification. This experiment is considered a high possibility for speeding up the diagnostic and treatments of COVID-19 individuals, reducing practitioners' efforts, and improving epidemic containment by utilizing ECG data. © 2022 IEEE.

Clinicians' Guide to Artificial Intelligence in Colon Capsule Endoscopy-Technology Made Simple.

Artificial intelligence (AI) applications have become widely popular across the healthcare ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project following the recent COVID pandemic's impact. It demonstrated its capability to relieve the national backlog in endoscopy. As a result, AI-assisted colon capsule video analysis has become gastroenterology's most active research area. However, with rapid AI advances, mastering these complex machine learning concepts remains challenging for healthcare professionals. This forms a barrier for clinicians to take on this new technology and embrace the new era of big data. This paper aims to bridge the knowledge gap between the current CCE system and the future, fully integrated AI system. The primary focus is on simplifying the technical terms and concepts in machine learning. This will hopefully address the general "fear of the unknown in AI" by helping healthcare professionals understand the basic principle of machine learning in capsule endoscopy and apply this knowledge in their future interactions and adaptation to AI technology. It also summarises the evidence of AI in CCE and its impact on diagnostic pathways. Finally, it discusses the unintended consequences of using AI, ethical challenges, potential flaws, and bias within clinical settings.

A novel proposed CNN-SVM architecture for ECG scalograms classification.

Nowadays, the number of sudden deaths due to heart disease is increasing with the coronavirus pandemic. Therefore, automatic classification of electrocardiogram (ECG) signals is crucial for diagnosis and treatment. Thanks to deep learning algorithms, classification can be performed without manual feature extraction. In this study, we propose a novel convolutional neural networks (CNN) architecture to detect ECG types. In addition, the proposed CNN can automatically extract features from images. Here, we classify a real ECG dataset using our proposed CNN which includes 34 layers. While this dataset is one-dimensional signals, these are transformed into images (scalograms) using continuous wavelet transform (CWT). In addition, the proposed CNN is compared to known architectures: AlexNet and SqueezeNet for classifying ECG images, and we find it more effective than others. This study, which not only performed CWT but also implemented short-time Fourier transform, examines the success in recognizing ECG types for the proposed CNN. Besides, different split methods: training and testing, and cross-validation are applied in this study. Eventually, CWT and cross-validation are the best pre-processing and split methods for the proposed CNN, respectively. Although the results are quite good, we benefit from support vector machines (SVM) to obtain the best algorithm and for detecting ECG types. Essentially, the main aim of the study increases classification results. In this way, the proposed CNN is utilized as deep feature extractor and combined with SVM. As a conclusion of this study, we achieve the highest accuracy of 99.21% from the proposed CNN-SVM when using CWT. Therefore, we can express that this framework can be used as an aid to clinicians for ECG-type identification.

Lung Classification for COVID-19

The year 2020 has seen the world being traumatized with the COVID-19 pandemic. COVID-19 virus had infected more than 100 million people and 2 million deaths worldwide. Many researchers race against time in producing vaccines and also used the latest technology in data analytics and artificial intelligence to help curb the pandemic. Deep features have shown to be an emerging area of research in various fields. Most recent deep works in the lung area focused on Convolutional Neural Networks (CNN). However, these have a drawback of over-classifying and not reflective of the real-world. Therefore, this article presented a cloud-based lung disease classification system, where medical practitioners can upload their patients' chest X-ray onto the cloud, and the system will classify if the disease is absent (normal) or present (abnormal). To test the disease, the system will then classify the lung infection as COVID-19 and non-COVID. Overall, the proposed system has obtained fairly good accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Lung Classification for COVID-19

The year 2020 has seen the world being traumatized with the COVID-19 pandemic. COVID-19 virus had infected more than 100 million people and 2 million deaths worldwide. Many researchers race against time in producing vaccines and also used the latest technology in data analytics and artificial intelligence to help curb the pandemic. Deep features have shown to be an emerging area of research in various fields. Most recent deep works in the lung area focused on Convolutional Neural Networks (CNN). However, these have a drawback of over-classifying and not reflective of the real-world. Therefore, this article presented a cloud-based lung disease classification system, where medical practitioners can upload their patients' chest X-ray onto the cloud, and the system will classify if the disease is absent (normal) or present (abnormal). To test the disease, the system will then classify the lung infection as COVID-19 and non-COVID. Overall, the proposed system has obtained fairly good accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Use of Deep Learning Application in Medical Devices

Artificial Intelligence (AI) is a system that helps machines to march with human abilities within daily lifestyles. Deep learning supported by AI can be an effective application within healthcare sector. This research has explained various aspects of Deep learning application that can be a major area of concern for pushing the development process of Indian medical sector that have lack of infrastructure and lack of capacity, to take less time to optimise the medical diagnosis process. This research has also investigated the advantages and disadvantages that medical sector might face while using deep learning applications. Deep learning applications under AI systems are used to classify objects. CNN model, Machine-learning tools, and other tools that use deep learning approach are effective to diagnose any disease and in medical image analysis process. Deep learning techniques are also used to detect heart disease and manage the data regarding the patients of heart diseases. Secondary data collection method has been used and a thematic analysis has been conducted in this research to describe and find various challenges that might have been engaged within deep learning process used in medical sectors of India. It has been found that, Deep Learning is used widely for COVID-19 medical image processing through a fully connected CNN model. As a result, the main finding states that deep learning application creates a major scope for the improvement in Indian medical sector. © 2022 IEEE.

Quantitative and Qualitative Analysis of 18 Deep Convolutional Neural Network (CNN) Models with Transfer Learning to Diagnose COVID-19 on Chest X-Ray (CXR) Images.

Coronavirus disease 2019 (COVID-19) is a disease caused by a novel strain of coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), severely affecting the lungs. Our study aims to combine both quantitative and qualitative analysis of the convolutional neural network (CNN) model to diagnose COVID-19 on chest X-ray (CXR) images. We investigated 18 state-of-the-art CNN models with transfer learning, which include AlexNet, DarkNet-19, DarkNet-53, DenseNet-201, GoogLeNet, Inception-ResNet-v2, Inception-v3, MobileNet-v2, NasNet-Large, NasNet-Mobile, ResNet-18, ResNet-50, ResNet-101, ShuffleNet, SqueezeNet, VGG-16, VGG-19, and Xception. Their performances were evaluated quantitatively using six assessment metrics: specificity, sensitivity, precision, negative predictive value (NPV), accuracy, and F1-score. The top four models with accuracy higher than 90% are VGG-16, ResNet-101, VGG-19, and SqueezeNet. The accuracy of these top four models is between 90.7% and 94.3%; the F1-score is between 90.8% and 94.3%. The VGG-16 scored the highest accuracy of 94.3% and F1-score of 94.3%. The majority voting with all the 18 CNN models and top 4 models produced an accuracy of 93.0% and 94.0%, respectively. The top four and bottom three models were chosen for the qualitative analysis. A gradient-weighted class activation mapping (Grad-CAM) was used to visualize the significant region of activation for the decision-making of image classification. Two certified radiologists performed blinded subjective voting on the Grad-CAM images in comparison with their diagnosis. The qualitative analysis showed that SqueezeNet is the closest model to the diagnosis of two certified radiologists. It demonstrated a competitively good accuracy of 90.7% and F1-score of 90.8% with 111 times fewer parameters and 7.7 times faster than VGG-16. Therefore, this study recommends both VGG-16 and SqueezeNet as additional tools for the diagnosis of COVID-19.

COVID-19 Detection Using Multimodal and Multi-model Ensemble Based Deep Learning Technique

COVID-19 is a fatal disease that threatens the people's health worldwide in the last few years. Although the testing techniques for COVID-19 had become more widespread, they still lack the speed and accuracy of disease pattern detection. Thanks to Artificial Intelligence (AI) as it can accelerate the detection process by deep learning techniques that can be used to achieve high performance in COVID-19 identification. Many types of Convolutional Neural Networks (CNN) as the most image classification deep learning techniques are used for automatically diagnosing this disease using X-ray or Computerized Tomography (CT-scan) medical images. The individual CNN types can obtain good results with a specific type of images like X-ray or CT-scan images in a certain dataset but, it could not give the same quality for other types of images or datasets. Through this paper, multiple standards model and custom CNN model have been merged using ensemble method to enhance the overall performance, while the accuracy of each model is a parameter in majority voting. Consequently, the proposed method will started with an initial simple classifier to classify between X-ray image and CT-image then followed by the ensemble model, and lasted by the decision making algorithm. Using different image types like X-ray and CT-scan images from different dataset sources enhance the overall performance as will be cleared in our results. The proposed model has three main parts: Multimodal imaging data, Multi-model based CNN structure, and decision-making diffusion based on the Multi-model output part. The main objective of using multiple models or multiple algorithms in detecting COVID-19 is to decrease the error percentage and increase the validation accuracy. Testing and validation results assure that the performance of the proposed method for COVID-19 chest X-rays and CT-scan images outperforms the individual and classical CNN learners' design. © 2022 IEEE.

Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021).

Hajj (pilgrimage) is a unique social and religious event in which many Muslims worldwide come to perform Hajj. More than two million people travel to Makkah, Saudi Arabia annually to perform various Hajj rituals for four to five days. However, given the recent outbreak of the coronavirus (COVID-19) and its variants, Hajj in the last 2 years 2020-2021 has been different because pilgrims were limited down to a few thousand to control and prevent the spread of COVID-19. This study employs a deep learning approach to investigate the impressions of pilgrims and others from within and outside the Makkah community during the 1442 AH Hajj season. Approximately 4,300 Hajj-related posts and interactions were collected from social media channels, such as Twitter and YouTube, during the Hajj season Dhul-Hijjah 1-13, 1442 (July 11-23, 2021). Convolutional neural networks (CNNs) and long short-term memory (LSTM) deep learning methods were utilized to investigate people's impressions from the collected data. The CNN-LSTM approach showed superior performance results compared with other widely used classification models in terms of F-score and accuracy. Findings revealed significantly positive sentiment rates for tweets collected from Mina and Arafa holy sites, with ratios exceeding 4 out of 5. Furthermore, the sentiment analysis (SA) rates for tweets about Hajj and pilgrims varied during the days of Hajj. Some were classified as positive tweets, such as describing joy at receiving the days of Hajj, and some were negative tweets, such as expressing the impression about the hot weather and the level of satisfaction for some services. Moreover, the SA of comments on several YouTube videos revealed positive classified comments, including praise and supplications, and negative classified comments, such as expressing regret that the Hajj was limited to a small number of pilgrims.

Masked Facial Recognition in Security Systems Using Transfer Learning.

The COVID-19 is a crisis of unprecedented magnitude, which has resulted in countless casualties and security troubles. In view of recent events of corona virus people are required to wear face masks to protect themselves from getting infected. As a result, a good portion of face (nose and mouth) is hidden by the mask and hence the facial recognition becomes difficult. Many organizations use facial recognition as a means of authentication. Researchers focus on developing rapid and efficient solutions to deal with the ongoing coronavirus pandemic by coming up with suggestions for handling the facial recognition problem. This research paper aims to identify the person, while the face is covered with a facial mask with only eyes and forehead being exposed. The first step involves marking the facial region. Next, using the data set, we will implement an object detection model YOLOv3 to identify unmasked and masked faces. The YOLO v3 object detection model is the best performing model with a detection time of 0.012 s, F1 score of 0.90 and mAP score of 0.92. Experimental results on Real-World Masked-Face-Data set show high recognition performance.

Efficient Grad-Cam-Based Model for COVID-19 Classification and Detection

Corona Virus (COVID-19) is a novel virus that crossed an animal-human barrier and emerged in Wuhan, China. Until now it has affected more than 119 million people. Detection of COVID-19 is a critical task and due to a large number of patients, a shortage of doctors has occurred for its detection. In this paper, a model has been suggested that not only detects the COVID-19 using X-ray and CT-Scan images but also shows the affected areas. Three classes have been defined;COVID-19, normal, and Pneumonia for X-ray images. For CT-Scan images, 2 classes have been defined COVID-19 and non-COVID-19. For classification purposes, pre-trained models like ResNet50, VGG-16, and VGG19 have been used with some tuning. For detecting the affected areas Gradient-weighted Class Activation Mapping (GradCam) has been used. As the X-rays and ct images are taken at different intensities, so the contrast limited adaptive histogram equalization (CLAHE) has been applied to see the effect on the training of the models. As a result of these experiments, we achieved a maximum validation accuracy of 88.10% with a training accuracy of 88.48% for CT-Scan images using the ResNet50 model. While for X-ray images we achieved a maximum validation accuracy of 97.31% with a training accuracy of 95.64% using the VGG16 model. © 2023 CRL Publishing. All rights reserved.

CNN Networks to Classify Cardiopulmonary Signals

At the moment, the world lives in a pandemic situation of COVID-19 and related variants, driving urgent needs for expanded assessments. A complementary support of related healthcare can be based on an intelligent system that can diagnose early onset of respiratory disorders. The convolutional neural networks (CNN) were implemented utilizing image data, reflecting bidimensional signals. Specifically, CNN has shown to be powerful tool in the context of cardiopulmonary sounds evaluation. The configurations of CNN contain convolutional layers to extract feature maps and fully connected layers to classify indicators of interest. Even though, learning algorithms use parameters like learning rate which can determine and attain CNN configuration less complex, with excellent results as reflected in the experiments we carried out, and which focused on achieved configuration of CNN with excellent results classifying heart sounds (HS) and lung sounds (LS).

Face Mask Detection Analysis for Covid19 Using CNN and Deep Learning

The planet was severely affected by Coronavirus Disease 2019. The wearing of masks in public places is a big way of protecting people. In addition, many providers of public service only require consumers to wear masks correctly. However, only a few studies are focused on image analysis on face mask detection. I propose in this paper, a high-precision and effective mask detector for Mask Detection Method. Recognition from faces is a popular and significant technology in recent years. Face alterations and the presence of different masks make it too much challenging. In the real-world, when a person is uncooperative with the systems such as in video surveillance then masking is further common scenarios. For these masks, current face recognition performance degrades. An abundant number of researches work has been performed for recognizing faces under different conditions like changing pose or illumination, degraded images, etc. Still, difficulties created by masks are usually disregarded. The primary concern to this work is about facial masks, and especially to enhance the recognition accuracy of different masked faces. A feasible approach has been proposed that consists of first detecting the facial regions. So, n orders to detect whether you are wearing a face mask to protect yourself, I decided to construct a simple and basic Convolutional Neural Network model, using TensorFlow and Keras library. With the prevailing pandemic of COVID-19, these systems will benefit many kinds of organizations worldwide. These types of systems are especially important. © 2022 IEEE.

The diagnosis of COVID-19 in CT images using hybrid machine learning approaches (CNN & SVM)

The new coronavirus disease (2019) has spread quickly as an acute respiratory distress syndrome (ARDS) among millions of individuals worldwide. Furthermore, the number of COVID-19 checking obtainable in hospitals is very limited as compared to the rising number of infections every day. As an outcome, an automatic detection system must be implemented as a quick diagnostic tool for preventing or reducing the spread of COVID-19 among humans. The present paper aims to propose an automated system by means of a hybrid Deep Learning ("convolutional neural network "(CNN)) and "support vector machine (SVM) " approach for identifying COVID-19 pneumonia-infected patients on the basis of chest computed tomography (746 CT images of "COVID-19" and "non-COVID-19"). The proposed system is composed of three phases. The first, pre-processing phase begins with converting CT images into greyscale level CT images of equal size (256×256). The "contrast limited adaptive histogram equalization" technology is adopted to enhance the intensity levels, and demonstrate the feature of lung tissue. It is also necessary to normalize the division of the image elements by 255 to make the values between 0 and 1, as this will speed up the processing process. The second phase, the CNN (SimpNet model), was applied as a deep feature extraction technique to identify CT samples. The SVM classifier and SoftMax function are employed in the third phase to classify COVID-19 pneumonia-infected patients. Specificity, Sensitivity, "F-score ", Accuracy, and "area under curve" are used as criteria to estimate the efficiency of the classification. The results showed a high accuracy rate of COVID-19 classification which reached (98%) and (99.1%) for CNN-SoftMax and CNN-SVM classifier, respectively in the tested dataset (225 CT images). © The Author 2022. This work is licensed under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) that allows others to share and adapt the material for any purpose (even commercially), in any medium with an acknowledgement of the work's authorship and initial publication in this journal.

The Impact of Using Convolutional Neural Networks in COVID-19 Tasks: A Survey

Artificial Intelligence (AI) is considered a robust tool that is widely used in different computer tasks. Machine Learning (ML) as an essential type of AI and deep learning (DL) is merely a branch of (ML). DL can mainly be helping to fast analysis of the medical images, especially the complex images, and this can speed up an early diagnosis of diseases. The Covid-19 pandemic has spread rapidly within societies, creating real panic for all people. Convolutional Neural Network (CNN) is a sub-class of DL which is used to classify medical images. Researchers have exploited the merits of CNNs to deal with COVID-19. This merits and diversity enabled researchers and workers in this field to devise new methods used to detect early cases, predict patients, diagnose patients, design vaccines and drugs and others. This paper aims to conduct a comprehensive survey of the previous works that used CNNs to implement different tasks associated to Covid-19 in order to enrich researchers and provide sufficient information for new works in the same field. © 2022 University of Bahrain. All rights reserved.

An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques.

Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare structures. The present tools to detect COVID-19 experience numerous shortcomings. Therefore, novel diagnostic tools are to be examined to enhance diagnostic accuracy and avoid the limitations of these tools. Earlier studies indicated multiple structures of cardiovascular alterations in COVID-19 cases which motivated the realization of using ECG data as a tool for diagnosing the novel coronavirus. This study introduced a novel automated diagnostic tool based on ECG data to diagnose COVID-19. The introduced tool utilizes ten deep learning (DL) models of various architectures. It obtains significant features from the last fully connected layer of each DL model and then combines them. Afterward, the tool presents a hybrid feature selection based on the chi-square test and sequential search to select significant features. Finally, it employs several machine learning classifiers to perform two classification levels. A binary level to differentiate between normal and COVID-19 cases, and a multiclass to discriminate COVID-19 cases from normal and other cardiac complications. The proposed tool reached an accuracy of 98.2% and 91.6% for binary and multiclass levels, respectively. This performance indicates that the ECG could be used as an alternative means of diagnosis of COVID-19.