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1.
Intelligent Systems with Applications ; : 200175, 2023.
Article in English | ScienceDirect | ID: covidwho-2165438

ABSTRACT

The Coronavirus disease (2019) has caused massive destruction of human lives and capital around the world. The latest variant Omicron is proved to be the most infectious of all its previous counterparts – Alpha, Beta and Delta. Various measures are identified, tested and implemented to minimize the attack on humans. Face masks are one of those measures that are shown to be very effective in containing the infection. However, it requires continuous monitoring for law enforcement. In the present manuscript, a detailed research investigation using different ablation studies is carried out to develop the framework for face mask recognition using pre-trained deep convolution neural networks (DCNN) models used in conjunction with a fast single layer feed-forward neural network (SLFNN) commonly known as Extreme Learning Machine (ELM) as classification technique. The ELM is well known for its real time data processing capabilities and has been successfully applied both for regression and classification problems of image processing and biomedical domain. It is for the first time that in this paper we have proposed the use of ELM as classifier for face mask detection. As a precursor to this, for feature selection, six pre-trained DCNNs such as Xception, Vgg16, Vgg19, ResNet50, ResNet 101 and ResNet152 are tested for this purpose. The best testing accuracy is obtained in case of ResNet152 transfer learning model used with ELM as the classifier. The performance evaluation through different ablation studies on testing accuracy explicitly proves that ResNet152 - ELM hybrid architecture is not only the best among the selected transfer learning models but also proves so when it is compared with several other classifiers used for the face mask detection operation. Through this investigation, novelty of the use of ResNet152 + ELM for face mask detection framework in real time domain is established.

2.
Front Immunol ; 13: 960985, 2022.
Article in English | MEDLINE | ID: covidwho-2154722

ABSTRACT

One of the primary tasks in vaccine design and development of immunotherapeutic drugs is to predict conformational B-cell epitopes corresponding to primary antibody binding sites within the antigen tertiary structure. To date, multiple approaches have been developed to address this issue. However, for a wide range of antigens their accuracy is limited. In this paper, we applied the transfer learning approach using pretrained deep learning models to develop a model that predicts conformational B-cell epitopes based on the primary antigen sequence and tertiary structure. A pretrained protein language model, ESM-1v, and an inverse folding model, ESM-IF1, were fine-tuned to quantitatively predict antibody-antigen interaction features and distinguish between epitope and non-epitope residues. The resulting model called SEMA demonstrated the best performance on an independent test set with ROC AUC of 0.76 compared to peer-reviewed tools. We show that SEMA can quantitatively rank the immunodominant regions within the SARS-CoV-2 RBD domain. SEMA is available at https://github.com/AIRI-Institute/SEMAi and the web-interface http://sema.airi.net.


Subject(s)
COVID-19 , Vaccines , Antigens , Epitopes, B-Lymphocyte , Humans , Immunodominant Epitopes , Machine Learning , SARS-CoV-2
3.
Procedia Comput Sci ; 204: 65-72, 2022.
Article in English | MEDLINE | ID: covidwho-2150430

ABSTRACT

A continuing outbreak of pneumonia-related disease novel, Coronavirus has been recorded worldwide and has become a global health problem. This research aims to generate a constructive training data set for a neural network to detect COVID-19 from X-ray images. The creation of medical images is an issue in the field of deep learning. Medical image datasets are frequently unbalanced; using such datasets to train a deep neural network model to correctly classify medical conditions typically leads to over-fitting the data on majority class samples. Data augmentation is commonly used in training data to expand the dataset. Data augmentation may not be beneficial in medical domains with limited data. This paper proposed a data generation model using a Deep Convolutional Generative adversarial network (DCGAN), which generates fake instances with comparable properties to the original data. The model's Fréchet Distance of Inception (FID) was 23.78, close to the original data. Deep transfer learning-based models VGG-16, Inceptionv3 and MobilNet, were chosen as the backbone for COVID-19 detection. The present study aims to increase the dataset using the DCGAN data augmentation technique to improve classifier performance.

4.
New Gener Comput ; : 1-17, 2022 Jun 16.
Article in English | MEDLINE | ID: covidwho-2148763

ABSTRACT

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.

5.
NeuroQuantology ; 20(16):2938-2944, 2022.
Article in English | EMBASE | ID: covidwho-2164836

ABSTRACT

In recent months, the fight against COVID-19 has grown into one of the most actively pursued anti-toxin treatment strategies worldwide. Correct medical reasoning and a swift response are essential to preventing the COVID-19 epidemic from taking an unexpected turn. Corona virus can be detected via RT-PCR, although chest X-ray techniques have been more effective and helpful in detecting the virus's effects. With an increasing number of people being detected with COVID and a larger number of X-rays being taken, it is now viable to use transfer learning to categorise the X-ray results. Covid19, bacterial pneumonia, and normal incident X-ray datasets have been combined to develop an automatic method for detecting the disease. Specifically, the objective of this study is to achieve better image classification results over state-of-the-art models like the Convolutional Neural Network (CNN) that were developed recently. The data sets were collected from freely accessible online medical sources. The results shows that significant biomarkers associated with Covid-19 illness can be identified using a combination of Transfer Learning and X-ray imaging. In our experiments, we found that the best accuracy was achieved using a combination of VGG16, Resnet50, and a Convolutional Layer, with respective values of 96.78, 98.66%, and 96.46 %. X-rays' potential for utility in diagnosis has grown as the failure rates of older, more established analytical methods have grown alarmingly high. Copyright © 2022, Anka Publishers. All rights reserved.

6.
Journal of Pharmaceutical Negative Results ; 13:4420-4424, 2022.
Article in English | EMBASE | ID: covidwho-2164829

ABSTRACT

The policies to manage the spread of diseases, tracking and predicting the growth of the diseases can effectively can be done by using Machine Learning techniques (ML). An ML can be built based on extended models that can be applied to diagnosis and find possible ways for the treatment of COVID-19 in worldwide countries. Machine learning techniques are used to provide the problems of real world by developing intelligence techniques. This survey targets on working procedure and affords the list of applications through deep convolution neural network, transfer learning, Support Vector Machine, and Linear Regression. Initially the researchers encourage the Machine learning algorithms for analyzing many domains to develop and innovates the new techniques with desirable advantages. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

7.
Indonesian Journal of Electrical Engineering and Computer Science ; 29(2):852-860, 2023.
Article in English | Scopus | ID: covidwho-2164231

ABSTRACT

Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2. RNN was used to classify data after extracting complicated characteristics from them using CNN. The VGG19-RNN design had the greatest accuracy of all of the networks with 97.8% accuracy. Gradient-weighted the class activation mapping (Grad-CAM) method was then used to show the decision-making areas of pictures that are distinctive to each class. In comparison to other current systems, the system produced promising findings, and it may be confirmed as additional samples become available in the future. For medical personnel, the examination revealed an excellent alternative way of diagnosing COVID-19. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

8.
30th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2022 ; : 1257-1268, 2022.
Article in English | Scopus | ID: covidwho-2162008

ABSTRACT

Digital twins are increasingly developed to support the development, operation, and maintenance of cyber-physical systems such as industrial elevators. However, industrial elevators continuously evolve due to changes in physical installations, introducing new software features, updating existing ones, and making changes due to regulations (e.g., enforcing restricted elevator capacity due to COVID-19), etc. Thus, digital twin functionalities (often built on neural network-based models) need to evolve themselves constantly to be synchronized with the industrial elevators. Such an evolution is preferred to be automated, as manual evolution is time-consuming and error-prone. Moreover, collecting sufficient data to re-train neural network models of digital twins could be expensive or even infeasible. To this end, we propose unceRtaInty-aware tranSfer lEarning enriched Digital Twins LATTICE, a transfer learning based approach capable of transferring knowledge about the waiting time prediction capability of a digital twin of an industrial elevator across different scenarios. LATTICE also leverages uncertainty quantification to further improve its effectiveness. To evaluate LATTICE, we conducted experiments with 10 versions of an elevator dispatching software from Orona, Spain, which are deployed in a Software in the Loop (SiL) environment. Experiment results show that LATTICE, on average, improves the Mean Squared Error by 13.131% and the utilization of uncertainty quantification further improves it by 2.71%. © 2022 ACM.

9.
2022 International Conference for Natural and Applied Sciences, ICNAS 2022 ; : 45-51, 2022.
Article in English | Scopus | ID: covidwho-2161403

ABSTRACT

Since the rapid spreading of covid-19 in 2019 in the whole world, it was conceded in 2020 as a pandemic. The long timeline of PCR tests and lack of test tool kits in many hospitals leads to fast infection according to the slow diagnosis. Various experiences of radiologists cause deferent in accurately detection lessons. This research suggested and designed a model based on utilizing the deep learning (DL) algorithms to detect the infection of covid-19 patients. Transfer learning VGG16 has been manipulated and used to solve the problem. Manipulating on VGG16 has been accomplished to achieve acceptable accuracy. The tuning on the last three layers of VGG16 architecture (dense layers) by replacing them with two layers (flatten layer and dense layer). The dense layer that is added deals with binary classification problems depending on the sigmoid function. This tuning serves the current study by speeding up the prediction of the model and also increasing the accuracy. A large COVID-19 CT scan slice dataset has been used to train and test the model. The result of testing reached 99.7% with a loss of 0.0085 and a validation loss of 0.0162. The obtained result proved that the system can help the radiologist accommodate the pandemic. © 2022 IEEE.

10.
2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 ; 2022-October:2237-2243, 2022.
Article in English | Scopus | ID: covidwho-2152540

ABSTRACT

This paper proposes transferred initialization with modified fully connected layers for COVID-19 diagnosis. Convolutional neural networks (CNN) achieved a remarkable result in image classification. However, training a high-performing model is a very complicated and time-consuming process because of the complexity of image recognition applications. On the other hand, transfer learning is a relatively new learning method that has been employed in many sectors to achieve good performance with fewer computations. In this research, the PyTorch pre-trained models (VGG19_bn and WideResNet -101) are applied in the MNIST dataset for the first time as initialization and with modified fully connected layers. The employed PyTorch pre-trained models were previously trained in ImageNet. The proposed model is developed and verified in the Kaggle notebook, and it reached the outstanding accuracy of 99.77% without taking a huge computational time during the training process of the network. We also applied the same methodology to the SIIM-FISABIO-RSNA COVID-19 Detection dataset and achieved 80.01% accuracy. In contrast, the previous methods need a huge compactional time during the training process to reach a high-performing model. Codes are available at the following link: github.com/dipuk0506/Spina1Net © 2022 IEEE.

11.
2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 ; 2022-October:2350-2357, 2022.
Article in English | Scopus | ID: covidwho-2152537

ABSTRACT

The COVID-19 rapid antigen self-test kits are widely administered in several countries to increase the testing frequency and reduce the load on clinics for in-person tests. Yet, the telehealth worker supervision is mandatory to ensure proper sampling procedure is followed and high-quality swab samples are taken. To reduce the load on the health workers in telehealth, we propose a system that eliminates the need for any human supervision by guiding the testers throughout the self-test to ensure the collection of high-quality swab samples. The proposed system takes a live video stream of the frontal face of a user as input and provides real-time instructions to do the self-test correctly with corrective actions when detecting wrong steps. This is mainly done using a collection of deep learning (DL) models. The system uses a novel swab position classification model, Small-MobileNetV2 with Depth-Wise Attention (S-MBNV2-DWAtt), to detect whether a swab is in one of the nostrils or not, which is an optimized version of MobileNetV2 in terms of parameter count and inference speed. The depth-wise attention block allows it to focus on specific parts of the images where the swabs would possibly lie. Lastly, a large-scale synthetic dataset is created to increase the generalization to a variety of swabs and users and a small real dataset is collected to finetune the model on scenes that are similar to the deployment scenarios. The proposed swab position classification model is found to have outstanding performance in terms of both accuracy and speed;it outperforms the ResNet and VGG architectures by 22.83% and 35.11% respectively on a real-world test set while operating at 25 FPS on CPU. © 2022 IEEE.

12.
7th International Conference on ICT for Sustainable Development, ICT4SD 2022 ; 517:399-407, 2023.
Article in English | Scopus | ID: covidwho-2148690

ABSTRACT

Livestreaming platforms are discernibly the most comprehensive sources of data in real time. Such websites enable users to broadcast content like the games which they are playing, while providing them the opportunity to interact with viewers watching the livestream. Twitch.tv is one of the most popular livestreaming platforms across the globe with millions of monthly active streamers and viewers. Owing to the COVID-19 pandemic, there has been a shift in the conventional lifestyle of the people, with them turning towards online alternatives like Twitch.tv for leisure. This change has led to an increase in the engagement of users in these livestreaming platforms by manifolds. Concurrently, a lot of data is generated from this sudden inflow, which can prove very useful in understanding the general consensus of the crowd. This data is very important, and there is a need to construe the true emotion of the people in real time, which is reflected in the comments made by them in the chat section of livestream. The streamers on Twitch.tv can consequently refine their content immediately based on the feedback that they can infer from the responses given by the users. But, due to the sheer volume of data and convoluted nature of the chat due to the use of emojis, emotes, and emoticons, there are bound to be inconsistencies, human errors, and other esoteric references which are exceedingly complex to dissect, making the task of language processing difficult and leading to incoherent results. Taking into account the hindrance posed by these issues, we have taken up the task to achieve fairly accurate emotion prediction by putting forward machine learning and deep learning techniques. This will involve the creation of a labelled dataset that can be used for training and evaluating the algorithms. Given how context-specific most comments are on the platform, this will be an extensive task. The project will also require the creation of an end-to-end system that performs emotion analysis and giving results in real time through feedback-loops. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

13.
Lecture Notes on Data Engineering and Communications Technologies ; 152:234-247, 2023.
Article in English | Scopus | ID: covidwho-2148629

ABSTRACT

The COVID-19 coronavirus is one of the devastating viruses according to the world health organization. This novel virus leads to pneumonia, which is an infection that inflames the lungs’ air sacs of a human. One of the methods to detect those inflames is by using x-rays for the chest. In this paper, a pneumonia chest x-ray detection based on generative adversarial networks (GAN) with a fine-tuned deep transfer learning for a limited dataset will be presented. The use of GAN positively affects the proposed model robustness and made it immune to the overfitting problem and helps in generating more images from the dataset. The dataset used in this research consists of 5863 X-ray images with two categories: Normal and Pneumonia. This research uses only 10% of the dataset for training data and generates 90% of images using GAN to prove the efficiency of the proposed model. Through the paper, AlexNet, GoogLeNet, Squeeznet, and Resnet18 are selected as deep transfer learning models to detect the pneumonia from chest x-rays. Those models are selected based on their small number of layers on their architectures, which will reflect in reducing the complexity of the models and the consumed memory and time. Using a combination of GAN and deep transfer models proved it is efficiency according to testing accuracy measurement. The research concludes that the Resnet18 is the most appropriate deep transfer model according to testing accuracy measurement and achieved 99% with the other performance metrics such as precision, recall, and F1 score while using GAN as an image augmenter. Finally, a comparison result was carried out at the end of the research with related work which used the same dataset except that this research used only 10% of original dataset. The presented work achieved a superior result than the related work in terms of testing accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

14.
New Gener Comput ; : 1-24, 2022 Nov 20.
Article in English | MEDLINE | ID: covidwho-2128609

ABSTRACT

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.

15.
Computers, Materials and Continua ; 74(2):4239-4259, 2023.
Article in English | Scopus | ID: covidwho-2146418

ABSTRACT

Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical states (COVID-19, TB, and normal cases), this study proposes an amalgam of image filtering, data-augmentation technique, transfer learning-based approach, and advanced deep-learning classifiers to effectively segregate these diseases. It first employed a generative adversarial network (GAN) and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise. Each pre-processed image is then converted into red, green, and blue (RGB) and Commission Internationale de l’Elcairage (CIE) color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50. Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network (RNN) classifiers for precise discrimination of three-clinical states. Comparative analysis showed that the proposed Bi-directional long-short-term-memory (Bi-LSTM) model dominated the long-short-term-memory (LSTM) network by attaining an overall accuracy of 98.22% for the three-class classification task, whereas LSTM hardly achieved 94.22% accuracy on the test dataset. © 2023 Tech Science Press. All rights reserved.

16.
International Journal on Advanced Science, Engineering and Information Technology ; 12(5):1895-1906, 2022.
Article in English | Scopus | ID: covidwho-2145806

ABSTRACT

COVID-19 still exists at an alarming level;hence, early diagnosis is important for treating and controlling this disease due to its rapid spread. The use of X-rays in medical image analysis can play an essential role in fast and affordable diagnosis. This study used a two-level feature selection in hybrid deep convolutional features obtained from the extraction of X-ray images. The transfer learning-based approach was implemented using five convolutional neural networks (CNNs) named VGG16, VGG19, ResNet50, InceptionV3, and Xception. The combination of two or three CNNs' performance as a feature extractor was then carefully analyzed. We selected the features obtained from multiple CNNs in a particular layer with a specified percentage of features in the first level for getting relevant features from various models. Then, we combined those features and did the second level of feature selection to select the most informative features. Both levels of feature selection were carried out using the light gradient boosting machine (LightGBM) algorithm. The final feature set has been used to classify COVID-19 and non-COVID-19 chest X-ray images using the support vector machines (SVM) classifier. The proposed model's performance was evaluated and analyzed on the open-access dataset. The highest accuracy was 99.80% using only 5% of the features extracted from ResNet50 and Xception. The other way of combining the ensemble of deep features and a few recent works for the classification of COVID-19 were also compared with the proposed model. As a result, our proposed model has achieved the best success rate for this dataset and may be deployed to support decision systems for radiologists © IJASEIT is licensed under a Creative Commons Attribution-Share Alike 4.0 International License

17.
24th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2022 ; 356:229-238, 2022.
Article in English | Scopus | ID: covidwho-2141606

ABSTRACT

Over the last months, dozens of artificial intelligence (AI) solutions for COVID-19 diagnosis based on chest X-ray image analysis have been proposed. All of them with very impressive sensitivity and specificity results. However, its generalization and translation to the clinical practice are rather challenging due to the discrepancies between domain distributions when training and test data come from different sources. Consequently, applying a trained model on a new data set may have a problem with domain adaptation leading to performance degradation. This research aims to study the impact of image pre-processing on pre-trained deep learning models to reduce the learning domain. The dataset used in this research consists of 5,000 X-ray images obtained from different sources under two categories: negative and positive COVID-19 detection. We implemented transfer learning in 3 popular convolutional neural networks (CNNs), including VGG16, VGG19, and DenseNet169. We repeated the study following the same structure for original and pre-processed images. The pre-processing method is based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) filter application and image registration. After evaluating the models, the CNNs that have been trained with pre-processed images obtained an accuracy score up to 1.2% better than the unprocessed ones. Furthermore, we can observe that in the 3 CNN models, the repeated misclassified images represent 40.9% (207/506) of the original image dataset with the erroneous result. In pre-processed ones, this percentage is 48.9% (249/509). In conclusion, image processing techniques can help to reduce the learning domain for deep learning applications. © 2022 The authors and IOS Press.

18.
2022 International Conference on Cloud Computing, Performance Computing, and Deep Learning, CCPCDL 2022 ; 12287, 2022.
Article in English | Scopus | ID: covidwho-2137317

ABSTRACT

During the COVID-19 pandemic, wearing gauze masks was proven to prevent people from infection. In public areas like shopping malls or schools need a way to supervise people wearing masks. This research aims to provide managers of public areas with an idea to solve this problem by GoogLeNet which is a type of convolutional neural network algorithm. Especially in crowded public areas, people should wear masks whether for their health or the health of others. These areas, such as stations and shopping malls, can only supervise people wearing masks at the entrance, but it is difficult to supervise people wearing masks inside buildings. As a result, many people will take off their masks or incorrectly wear them indoors due to heat. In this case, we consider how to intercept everyone's avatars in the video on closed-circuit television. Use neural network training algorithms to monitor everyone's mask-wearing situation. And promptly warn people who wear masks incorrectly or who do not wear masks. © 2022 SPIE.

19.
3rd International Conference on Next Generation Computing Applications, NextComp 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2136450

ABSTRACT

This paper presents an explainable deep learning network to classify COVID from non-COVID based on 3D CT lung images. It applies a subset of the data for MIA-COV19 challenge through the development of 3D form of Vision Transformer deep learning architecture. The data comprise 1924 subjects with 851 being diagnosed with COVID, among them 1,552 being selected for training and 372 for testing. While most of the data volume are in axial view, there are a number of subjects' data are in coronal or sagittal views with 1 or 2 slices are in axial view. Hence, while 3D data based classification is investigated, in this competition, 2D axial-view images remains the main focus. Two deep learning methods are studied, which are vision transformer (ViT) based on attention models and DenseNet that is built upon conventional convolutional neural network (CNN). Initial evaluation results indicates that ViT performs better than DenseNet with F1 scores being 0.81 and 0.72 respectively. (Codes are available at GitHub at https://github.com/xiaohong1/COVID-ViT). This paper illustrates that vision transformer performs the best in comparison to the other current state of the art approaches in classification of COVID from CT lung images. © 2022 IEEE.

20.
4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2136364

ABSTRACT

The fast proliferation of the coronavirus disease 2019 (COVID19) has pushed many countries' healthcare systems to the brink of disaster. It has become a necessity to automate the screening procedures to reduce the ongoing cost to the healthcare systems. Although the use of the Convolutional Neural Networks (CNNs) is gaining attention in the field of COVID19 diagnosis based on medical images, these models have disadvantages due to their image-specific inductive bias, which contradict to the Vision Transformer (ViT). This paper conducts comparative study of the use of the three most established CNN models and a ViT to deal with the classification of COVID19 and Non-COVID19 cases. This study uses 2481 computed tomography (CT) images of 1252 COVID19 and 1229 Non-COVID19 patients. Confusion metrics and performance metrics were used to analyze the models. The experimental results show all the pre-trained CNNs (VGG16, ResNet50, and IncetionV3)outperformed the pre-trained ViT model, with InceptionV3 as the best performing model (99.20% of accuracy). © 2022 IEEE.

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