scholarly journals Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images

2021 ◽  
Author(s):  
Kamal KC ◽  
Zhendong Yin ◽  
Mingyang Wu ◽  
Zhilu Wu
Keyword(s):  
Neural Network ◽  
Bacterial Pneumonia ◽  
State Of The Art ◽  
Test Accuracy ◽  
Computationally Efficient ◽  
X Ray ◽  
The World ◽  
Data Problem ◽  
Chest X Ray ◽  
Novel Coronavirus

AbstractThe COVID-19, novel coronavirus or SARS-Cov-2, has claimed hundreds of thousands of lives and affected millions of people all around the world with the number of deaths and infections growing exponentially. Deep convolutional neural network (DCNN) has been a huge milestone for image classification task including medical images. Transfer learning of state-of-the-art models have proven to be an efficient method of overcoming deficient data problem. In this paper, a thorough evaluation of eight pre-trained models is presented. Training, validating, and testing of these models were performed on chest X-ray (CXR) images belonging to five distinct classes, containing a total of 760 images. Fine-tuned models, pre-trained in ImageNet dataset, were computationally efficient and accurate. Fine-tuned DenseNet121 achieved a test accuracy of 98.69% and macro f1-score of 0.99 for four classes classification containing healthy, bacterial pneumonia, COVID-19, and viral pneumonia, and fine-tuned models achieved higher test accuracy for three-class classification containing healthy, COVID-19, and SARS images. The experimental results show that only 62% of total parameters were retrained to achieve such accuracy.

scholarly journals Performance Result for Detection of COVID-19 using Deep Learning

2020 ◽  
Vol 9 (7) ◽  
pp. 699-703
Keyword(s):  
Neural Network ◽  
Classification Model ◽  
Classification Models ◽  
X Ray ◽  
The World ◽  
Rectified Linear Unit ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
The Masses ◽  
Fully Connected

The 2019 novel coronavirus (COVID-19), which has sprawled fleetly among masses residing in distant nations, had a prefatory juncture in China. From both a safeness and a lucrative outlook, it has staggered the world with its hasty diffusion with conjectural vicious generic repercussions for the masses. Consequent to the escalating cases daily, there is a constricted fraction of COVID-19 inspection kits acquirable in healthcare institutions. Ergo, to obviate COVID-19 propagating betwixt masses, it is imperative to enforce an instinctive unveiling network as a prompt jack legging diagnosis appendage. The contemplated method embroils a convolutional neural network- based model, namely ResNet50, concerted with a Fully Connected Layer (FCL), reinforced by Rectified Linear Unit (ReLU) for the unearthing of coronavirus pneumonia imparted sufferer by harnessing chest X-ray radiographs. The endorsed classification model, i.e. resnet50 affirmed by FCL and ReLU, compassed accuracy of 94% for unearthing COVID-19. When equated to diverse classification models, the purported model is preeminent. The aftereffect is premised on the attested X-ray images from the data appropriable in the arsenal of Kaggle

scholarly journals MFDNN: Multi-channel feature deep neural network algorithm to identify Covid19 chest X-ray images

2021 ◽  
Author(s):  
Liangrui Pan ◽  
boya ji ◽  
Xiaoqi wang ◽  
shaoliang peng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Test Accuracy ◽  
Learning Networks ◽  
X Ray ◽  
Network Algorithm ◽  
Life Saving ◽  
Chest X Ray ◽  
Neural Network Algorithm

The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID-19) is life-saving important for both patients and doctors. This research proposed a multi-channel feature deep neural network algorithm to screen people infected with COVID-19. The algorithm integrates data oversampling technology and a multi-channel feature deep neural network model to carry out the training process in an end-to-end manner. In the experiment, we used a publicly available CXI database with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images. Compared with traditional deep learning models (Densenet201, ResNet50, VGG19, GoogLeNet), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Secondly, compared with the latest CoroDet model, the MFDNN algorithm is 1.91% higher than the CoroDet model in the experiment of detecting the four categories of COVID19 infected persons. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.

scholarly journals CovidMulti-Net: A Parallel-Dilated Multi Scale Feature Fusion Architecture for the Identification of COVID-19 Cases from Chest X-ray Images

2021 ◽  
Author(s):  
Md. Saikat Islam Khan ◽  
Anichur Rahman ◽  
Md. Razaul Karim ◽  
Nasima Islam Bithi ◽  
Shahab Band ◽  
...  
Keyword(s):  
Healthcare Professionals ◽  
Feature Fusion ◽  
State Of The Art ◽  
Research Community ◽  
Diagnostic Model ◽  
X Ray ◽  
Multi Scale ◽  
The World ◽  
Chest X Ray ◽  
Learning Concept

The COVID-19 pandemic is an emerging respiratory infectious disease, having a significant impact on the health and life of many people around the world. Therefore, early identification of COVID-19 patients is the fastest way to restrain the spread of the pandemic. However, as the number of cases grows at an alarming pace, most developing countries are now facing a shortage of medical resources and testing kits. Besides, using testing kits to detect COVID-19 cases is a time-consuming, expensive, and cumbersome procedure. Faced with these obstacles, most physicians, researchers, and engineers have advocated for the advancement of computer-aided deep learning models to assist healthcare professionals in quickly and inexpensively recognize COVID-19 cases from chest X-ray (CXR) images. With this motivation, this paper proposes a CovidMulti-Net architecture based on the transfer learning concept to classify COVID-19 cases from normal and other pneumonia cases using three publicly available datasets that include 1341, 1341, and 446 CXR images from healthy samples and 902, 1564, and 1193 CXR images infected with Viral Pneumonia, Bacterial Pneumonia, and COVID-19 diseases. In the proposed framework, features from CXR images are extracted using three well-known pre-trained models, including DenseNet-169, ResNet-50, and VGG-19. The extracted features are then fed into a concatenate layer, making a robust hybrid model. The proposed framework achieved a classification accuracy of 99.4%, 95.2%, and 94.8% for 2-Class, 3-Class, and 4-Class datasets, exceeding all the other state-of-the-art models. These results suggest that the CovidMulti-Net frameworks ability to discriminate individuals with COVID-19 infection from healthy ones and provides the opportunity to be used as a diagnostic model in clinics and hospitals. We also made all the materials publicly accessible for the research community at: https://github.com/saikat15010/CovidMulti-Net-Architecture.git.

scholarly journals Improving Coronavirus (COVID-19) Diagnosis using Deep Transfer Learning

2020 ◽  
Author(s):  
Arshia Rehman ◽  
Saeeda Naz ◽  
Ahmed Khan ◽  
Ahmad Zaib ◽  
Imran Razzak
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Bacterial Pneumonia ◽  
State Of The Art ◽  
High Accuracy ◽  
X Rays ◽  
Early Screening ◽  
X Ray ◽  
Learning Techniques ◽  
The World

AbstractBackgroundCoronavirus disease (COVID-19) is an infectious disease caused by a new virus. Exponential growth is not only threatening lives, but also impacting businesses and disrupting travel around the world.AimThe aim of this work is to develop an efficient diagnosis of COVID-19 disease by differentiating it from viral pneumonia, bacterial pneumonia and healthy cases using deep learning techniques.MethodIn this work, we have used pre-trained knowledge to improve the diagnostic performance using transfer learning techniques and compared the performance different CNN architectures.ResultsEvaluation results using K-fold (10) showed that we have achieved state of the art performance with overall accuracy of 98.75% on the perspective of CT and X-ray cases as a whole.ConclusionQuantitative evaluation showed high accuracy for automatic diagnosis of COVID-19. Pre-trained deep learning models develop in this study could be used early screening of coronavirus, however it calls for extensive need to CT or X-rays dataset to develop a reliable application.

scholarly journals COVID Faster R-CNN: A Novel Framework to Diagnose Novel Coronavirus Disease (COVID-19) in X-Ray Images

2020 ◽  
Author(s):  
Kabid Hassan Shibly ◽  
Samrat Kumar Dey ◽  
Md. Tahzib Ul Islam ◽  
Md. Mahbubur Rahman
Keyword(s):  
Imaging Techniques ◽  
Relevant Information ◽  
Human Interaction ◽  
Initial Assessment ◽  
Critical Approach ◽  
X Ray ◽  
X Ray Imaging ◽  
The World ◽  
Chest X Ray ◽  
Novel Coronavirus

COVID-19 or novel coronavirus disease, which has already been declared as a worldwide pandemic, at first had an outbreak in a small town of China, named Wuhan. More than two hundred countries around the world have already been affected by this severe virus as it spreads by human interaction. Moreover, the symptoms of novel coronavirus are quite similar to the general flu. Screening of infected patients is considered as a critical step in the fight against COVID-19. Therefore, it is highly relevant to recognize positive cases as early as possible to avoid further spreading of this epidemic. However, there are several methods to detect COVID-19 positive patients, which are typically performed based on respiratory samples and among them one of the critical approach which is treated as radiology imaging or X-Ray imaging. Recent findings from X-Ray imaging techniques suggest that such images contain relevant information about the SARS-CoV-2 virus. In this article, we have introduced a Deep Neural Network (DNN) based Faster Regions with Convolutional Neural Networks (Faster R-CNN) framework to detect COVID-19 patients from chest X-Ray images using available open-source dataset. Our proposed approach provides a classification accuracy of 97.36%, 97.65% of sensitivity, and a precision of 99.28%. Therefore, we believe this proposed method might be of assistance for health professionals to validate their initial assessment towards COVID-19 patients.

scholarly journals COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
Hossein Aboutalebi ◽  
Maya Pavlova ◽  
Mohammad Javad Shafiee ◽  
Ali Sabri ◽  
Amer Alaref ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Clinical Decision ◽  
Severity Assessment ◽  
Clinical Workflow ◽  
X Ray ◽  
Number Of Patients ◽  
The World ◽  
Chest X Ray

The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing and handling patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest X-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient’s chest. More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 SARS-CoV-2 positive and negative patient cases into a custom network architecture for severity assessment. Experimental results using the RSNA RICORD dataset showed that the proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients. Furthermore, radiologist validation on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, showed consistency between radiologist interpretation and critical factors leveraged by COVID-Net CXR-S for severity assessment. While not a production-ready solution, the ultimate goal for the open source release of COVID-Net CXR-S is to act as a catalyst for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative new clinical decision support solutions for helping clinicians around the world manage the continuing pandemic.

scholarly journals COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images

2021 ◽  
Author(s):  
Hossein Aboutalebi ◽  
Maya Pavlova ◽  
Mohammad Javad Shafiee ◽  
Ali Sabri ◽  
Amer Alaref ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Clinical Decision ◽  
Severity Assessment ◽  
Clinical Workflow ◽  
X Ray ◽  
Number Of Patients ◽  
The World ◽  
Chest X Ray

Abstract The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest x-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient's chest. More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 patient cases into a custom network architecture for severity assessment. Experimental results with a multi-national patient cohort curated by the Radiological Society of North America (RSNA) RICORD initiative showed that the proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients. Furthermore, radiologist validation on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, showed consistency between radiologist interpretation and critical factors leveraged by COVID-Net CXR-S for severity assessment. While not a production-ready solution, the ultimate goal for the open source release of COVID-Net CXR-S is to act as a catalyst for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative new clinical decision support solutions for helping clinicians around the world manage the continuing pandemic.

scholarly journals KL-MOB Automated Covid-19 Recognition Using a Novel Approach Based on Image Enhancement and a Modified MobileNet CNN

2021 ◽  
Author(s):  
Mundher Mohammed Taresh ◽  
Ning bo Zhu ◽  
Asaad Shakir Hameed ◽  
Modhi Lafta Mutar ◽  
Talal Ahmed Ali Ali Ahmed Ali Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Loss Function ◽  
X Ray ◽  
Kl Divergence ◽  
Fine Grained ◽  
Novel Approach ◽  
Starting Point ◽  
The World ◽  
Novel Coronavirus ◽  
Sensitivity Specificity

The emergence of the novel coronavirus pneumonia (Covid-19) pandemic at the end of 2019 led to chaos worldwide. The world breathed a sigh of relief when some countries announced that they had obtained the appropriate vaccine and gradually began to distribute it. Nevertheless, the emergence of another wave of this disease has returned us to the starting point. At present, early detection of infected cases has been the paramount concern of both specialists and health researchers. This paper aims to detect infected patients through chest X-ray images. The large dataset available online for Covid-19 (COVIDx) was used in this research. The dataset consists of 2,128 x-ray images of Covid-19 cases, 8,066 normal cases, and 5,575 cases of pneumonia. A hybrid algorithm was applied to improve image quality before conducting the neural network training process. This algorithm consisted of combining two different noise reduction filters in the images, followed by a contrast enhancement algorithm. In this paper, for Covid-19 detection, a novel convolution neural network (CNN) architecture, KL-MOB (Covid-19 detection network based on MobileNet structure), was proposed. KL-MOB performance was boosted by adding the Kullback Leibler (KL) divergence loss function at the end when trained from scratch. The Kullback-Leibler (KL) divergence loss function was adopted as content-based image retrieval and fine-grained classification to improve the quality of image representation. This paper yielded impressive results, overall benchmark accuracy, sensitivity, specificity, and precision of 98.7%, 98.32%, 98.82%, and 98.37%, respectively. The promising results in this research may enable other researchers to develop modern and innovative methods to aid specialists. The tremendous potential of the method proposed in this research can also be utilized to detect Covid-19 quickly and safely in patients throughout the world.

scholarly journals KL-MOB: automated COVID-19 recognition using a novel approach based on image enhancement and a modified MobileNet CNN

2021 ◽  
Vol 7 ◽  
pp. e694
Author(s):  
Mundher Mohammed Taresh ◽  
Ningbo Zhu ◽  
Talal Ahmed Ali Ali ◽  
Mohammed Alghaili ◽  
Asaad Shakir Hameed ◽  
...  
Keyword(s):  
Neural Network ◽  
Loss Function ◽  
X Ray ◽  
Infected People ◽  
Kl Divergence ◽  
Novel Approach ◽  
Starting Point ◽  
The World ◽  
Novel Coronavirus ◽  
Sensitivity Specificity

The emergence of the novel coronavirus pneumonia (COVID-19) pandemic at the end of 2019 led to worldwide chaos. However, the world breathed a sigh of relief when a few countries announced the development of a vaccine and gradually began to distribute it. Nevertheless, the emergence of another wave of this pandemic returned us to the starting point. At present, early detection of infected people is the paramount concern of both specialists and health researchers. This paper proposes a method to detect infected patients through chest x-ray images by using the large dataset available online for COVID-19 (COVIDx), which consists of 2128 X-ray images of COVID-19 cases, 8,066 normal cases, and 5,575 cases of pneumonia. A hybrid algorithm is applied to improve image quality before undertaking neural network training. This algorithm combines two different noise-reduction filters in the image, followed by a contrast enhancement algorithm. To detect COVID-19, we propose a novel convolution neural network (CNN) architecture called KL-MOB (COVID-19 detection network based on the MobileNet structure). The performance of KL-MOB is boosted by adding the Kullback–Leibler (KL) divergence loss function when trained from scratch. The KL divergence loss function is adopted for content-based image retrieval and fine-grained classification to improve the quality of image representation. The results are impressive: the overall benchmark accuracy, sensitivity, specificity, and precision are 98.7%, 98.32%, 98.82% and 98.37%, respectively. These promising results should help other researchers develop innovative methods to aid specialists. The tremendous potential of the method proposed herein can also be used to detect COVID-19 quickly and safely in patients throughout the world.

scholarly journals A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

2021 ◽  
Vol 11 (19) ◽  
pp. 9023
Author(s):  
Najam-ur Rehman ◽  
Muhammad Sultan Zia ◽  
Talha Meraj ◽  
Hafiz Tayyab Rauf ◽  
Robertas Damaševičius ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
State Of The Art ◽  
Detection System ◽  
Classification Methods ◽  
Single Class ◽  
X Ray ◽  
Computer Aided ◽  
Chest X Ray ◽  
Fully Connected

Chest diseases can be dangerous and deadly. They include many chest infections such as pneumonia, asthma, edema, and, lately, COVID-19. COVID-19 has many similar symptoms compared to pneumonia, such as breathing hardness and chest burden. However, it is a challenging task to differentiate COVID-19 from other chest diseases. Several related studies proposed a computer-aided COVID-19 detection system for the single-class COVID-19 detection, which may be misleading due to similar symptoms of other chest diseases. This paper proposes a framework for the detection of 15 types of chest diseases, including the COVID-19 disease, via a chest X-ray modality. Two-way classification is performed in proposed Framework. First, a deep learning-based convolutional neural network (CNN) architecture with a soft-max classifier is proposed. Second, transfer learning is applied using fully-connected layer of proposed CNN that extracted deep features. The deep features are fed to the classical Machine Learning (ML) classification methods. However, the proposed framework improves the accuracy for COVID-19 detection and increases the predictability rates for other chest diseases. The experimental results show that the proposed framework, when compared to other state-of-the-art models for diagnosing COVID-19 and other chest diseases, is more robust, and the results are promising.

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