scholarly journals CCBlock based on deep learning for diagnosis COVID-19 in chest x-ray image

2020 ◽  
Author(s):  
Ali Al-Bawi ◽  
Karrar Ali Al-Kaabi ◽  
Mohammed Jeryo ◽  
Ahmad Al-Fatlawi
Keyword(s):  
Well Being ◽  
Healthy People ◽  
Pcr Analysis ◽  
The Novel ◽  
X Ray ◽  
Detection Systems ◽  
Testing Dataset ◽  
Rapid Spread ◽  
Chest X Ray ◽  
Novel Coronavirus

Abstract Propose: Troubling countries one after another, the COVID-19 pandemic has dramatically affected the health and well-being of the world's population. The disease may continue to persist more extensively due to the increasing number of new cases daily, the rapid spread of the virus, and delay in the PCR analysis results. Therefore, it is necessary to consider developing assistive methods for detecting and diagnosing the COVID-19 to eradicate the spread of the novel coronavirus among people. Based on convolutional neural networks (CNNs), automated detection systems have shown promising results of diagnosing patients with the COVID-19 through radiography; thus, they are introduced as a workable solution to the COVID-19 diagnosis.Materials and Methods: Based on the enhancement of the classical visual geometry group (VGG) network with the convolutional COVID block (CCBlock), an efficient screening model was proposed in this study to diagnose and distinguish patients with the COVID-19 from those with pneumonia and the healthy people through radiography. The model testing dataset included 1,828 x-ray images available on public platforms. 310 images were showing confirmed COVID-19 cases, 864 images indicating pneumonia cases, and 654 images showing healthy people.Results: According to the test results, enhancing the classical VGG network with radiography provided the highest diagnosis performance and overall accuracy of 98.52% for two classes as well as accuracy of 95.34% for three classes.Conclusions: According to the results, using the enhanced VGG deep neural network can help radiologists automatically diagnose the COVID-19 through radiography.

scholarly journals CCBlock based on deep learning for diagnosis COVID-19 in chest x-ray image

2020 ◽  
Author(s):  
Ali Al-Bawi ◽  
Karrar Ali Al-Kaabi ◽  
Mohammed Jeryo ◽  
Ahmad Al-Fatlawi
Keyword(s):  
Neural Network ◽  
Well Being ◽  
Healthy People ◽  
Alternative Methods ◽  
Pcr Analysis ◽  
Early Screening ◽  
Data Set ◽  
X Ray ◽  
Chest X Ray ◽  
Detection And Diagnosis

Abstract Purpose: COVID-19 pandemic continues to hit countries one after the other and has dramatically affected the health and well-being of the world's population. With the daily increase in the number of people with this disease, the impressive speed of spread and the delay in the results of PCR analysis, it may cause the disease to spread more broadly. Therefore it is necessary to consider finding alternative methods of detection and diagnosis COVID-19 to prohibit the spread of the disease among people. Convolutional Neural Network (CNN) automated detection systems have shown auspicious results in detecting patients with COVID-19 through radiography; thus, we suggest them as an alternative option to diagnose COVID-19.Method: In this study, an early screening model based on the enhancement of classical Visual Geometry Group Network (VGG) with Convolutional Covid Block (CCBlock) was proposed to detect and distinguish COVID-19 from Pneumonia, and healthy people using chest X-ray radiographs. The data set used for model testing is the x-ray images available on public platforms, which consist of 1,828 x-ray images, including 310 images for confirmed COVID-19 patients, 864 images for pneumonia patients, and 654 images for healthy people.Results: The experiment result of the dataset showed that the added enhancements to the classical VGG network with X-ray imaging provide the highest detection performance and overall accuracy of 98.52% for two classes and 95.34% accuracy for three classes.Conclusions: Considering the achievement results obtained, it was found that utilizing the enhanced VGG deep neural network helps radiologists automatically diagnose COVID-19 in X-ray images.

scholarly journals The use of lung ultrasound during the COVID-19 pandemic

2020 ◽  
Vol 27 (SP1) ◽  
pp. e64-e75
Author(s):  
Aly Youssef ◽  
Marta Cavalera ◽  
Carlotta Azzarone ◽  
Carla Serra ◽  
Elena Brunelli ◽  
...  
Keyword(s):  
Health System ◽  
Pulmonary Infection ◽  
Lung Ultrasound ◽  
The Novel ◽  
Chain Reaction ◽  
X Ray ◽  
Rapid Learning ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Novel Coronavirus

The novel coronavirus disease (COVID-19) is a challenge to every health system. Unfortunately, it is unlikely that this pandemic will disappear soon. No health system, with its present resources and workflow, is capable enough to deal with a full-blown wave of this pandemic. Acquisition of specific new skills may be fundamental in delivering appropriate health care for our patients. The gold standard for diagnosis of the COVID-19 infection is real-time reverse transcription polymerase chain reaction. Radiological investigations (chest X-ray or high-resolution computerized tomography [CT]) can be helpful both for diagnosis and management, but they have many limitations. Ultrasound has been suggested as a reliable and accurate tool for assessing the lungs in COVID-19 patients. Lung ultrasound (LUS) can show specific signs of inter-stitial pneumonia, which is characteristic of COVID-19 pulmonary infection. In addition, nonradiologist specialists with experience in ultrasound can be trained on LUS with a relatively rapid learning curve. In pregnancy, LUS can be particularly useful due to the avoidance of exposure to ionizing radiation. In this review, we present the advantages, techniques, and limitations of the use of LUS during the COVID-19 pandemic, with specific focus on pregnancy.

scholarly journals Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Prashant Kumar Shukla ◽  
Jasminder Kaur Sandhu ◽  
Anamika Ahirwar ◽  
Deepika Ghai ◽  
Priti Maheshwary ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Diagnostic Testing ◽  
The Novel ◽  
Screening Process ◽  
X Ray ◽  
Multiobjective Genetic Algorithm ◽  
Global Pandemic ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
Fold Cross Validation

COVID-19 is a new disease, caused by the novel coronavirus SARS-CoV-2, that was firstly delineated in humans in 2019.Coronaviruses cause a range of illness in patients varying from common cold to advanced respiratory syndromes such as Severe Acute Respiratory Syndrome (SARS-CoV) and Middle East Respiratory Syndrome (MERS-CoV). The SARS-CoV-2 outbreak has resulted in a global pandemic, and its transmission is increasing at a rapid rate. Diagnostic testing and approaches provide a valuable tool for doctors and support them with the screening process. Automatic COVID-19 identification in chest X-ray images can be useful to test for COVID-19 infection at a good speed. Therefore, in this paper, a framework is designed by using Convolutional Neural Networks (CNN) to diagnose COVID-19 patients using chest X-ray images. A pretrained GoogLeNet is utilized for implementing the transfer learning (i.e., by replacing some sets of final network CNN layers). 20-fold cross-validation is considered to overcome the overfitting quandary. Finally, the multiobjective genetic algorithm is considered to tune the hyperparameters of the proposed COVID-19 identification in chest X-ray images. Extensive experiments show that the proposed COVID-19 identification model obtains remarkably better results and may be utilized for real-time testing of patients.

scholarly journals COVID-19/Pneumonia Classification Based on Guided Attention

2021 ◽  
Author(s):  
Viacheslav Danilov ◽  
Alex Karpovsky ◽  
Alexander Kirpich ◽  
Diana Litmanovich ◽  
Dato Nefaridze ◽  
...  
Keyword(s):  
The Novel ◽  
New Techniques ◽  
Robust Solution ◽  
X Ray ◽  
Machine Learning Methods ◽  
Traditional Classification ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
Different Sources ◽  
Modern Machine

Abstract With the novel coronavirus 19 (COVID-19) continually having a devastating effect around the globe, many scientists and clinicians are actively seeking to develop new techniques to assist with the tackling of this disease. Modern machine learning methods have shown promise in their adoption to assist the health care industry through their data and analytics-driven decision making, inspiring researchers to develop new angles to fight the virus. In this paper, we aim to develop a robust method for the detection of COVID-19 by utilizing patients' chest X-ray images. Despite recent progress, scarcity of data has thus far limited the development of a robust solution. We extend upon existing work by combing publicly available data across 5 different sources and carefully annotating the comprising images into three categories: normal, pneumonia, and COVID-19. To achieve a high classification accuracy, we propose a training pipeline based on the directed guidance of traditional classification networks, where the guidance is directed by an external segmentation network. Through this network, we observed that the widely used, standard networks can achieve an accuracy comparable to tailor-made models specifically for COVID-19, furthermore one network, VGG-16, outperformed the best of the tailor-made models.

scholarly journals Automated COVID-19 Detection from Chest X-Ray Images : A High Resolution Network (HRNet)Approach

2020 ◽  
Author(s):  
Sifat Ahmed ◽  
Tonmoy Hossain ◽  
Oishee Bintey Hoque ◽  
Sujan Sarker ◽  
Sejuti Rahman ◽  
...  
Keyword(s):  
High Resolution ◽  
Well Being ◽  
X Rays ◽  
X Ray ◽  
Pcr Diagnosis ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
And Control ◽  
Learning Architectures ◽  
Global Population

The pandemic, originated by novel coronavirus 2019 (COVID-19), continuing its devastating effect on the health, well-being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID-19 in the human body, to constraint the exposure and control the spread of the virus. Chest X-Rays are one of the noninvasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time-consuming. In this work, we propose an automated COVID-19 classifier, utilizing available COVID and non-COVID X-Ray datasets, along with High Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes. To evaluate the proposed dataset, several baseline experiments have been performed employing numerous deep learning architectures. With extensive experiment, we got 99.26% accuracy, 98.53% sensitivity, and 98.82% specificity with HRNet which surpasses the performances of the existing models. Our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X-Ray images into the diagnosis of the disease.

scholarly journals Detection of COVID-19 from Chest X-ray and CT Scan Images using Improved Stacked Sparse Autoencoder

2021 ◽  
Vol 29 (3) ◽  
Author(s):  
Syahril Ramadhan Saufi ◽  
Muhd Danial Abu Hasan ◽  
Zair Asrar Ahmad ◽  
Mohd Salman Leong ◽  
Lim Meng Hee
Keyword(s):  
Ct Scan ◽  
The Novel ◽  
Essential Information ◽  
X Ray ◽  
Chest X Ray ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder ◽  
Novel Coronavirus ◽  
Deep Learning Model ◽  
Chest Ct Scan

The novel Coronavirus 2019 (COVID-19) has spread rapidly and has become a pandemic around the world. So far, about 44 million cases have been registered, causing more than one million deaths worldwide. COVID-19 has had a devastating impact on every nation, particularly the economic sector. To identify the infected human being and prevent the virus from spreading further, easy, and precise screening is required. COVID-19 can be potentially detected by using Chest X-ray and computed tomography (CT) images, as these images contain essential information of lung infection. This radiology image is usually examined by the expert to detect the presence of COVID-19 symptom. In this study, the improved stacked sparse autoencoder is used to examine the radiology images. According to the result, the proposed deep learning model was able to achieve a classification accuracy of 96.6% and 83.0% for chest X-ray and chest CT-scan images, respectively.

scholarly journals Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified MobileNetV2 and LIME

Healthcare ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1099
Author(s):  
Md Manjurul Ahsan ◽  
Redwan Nazim ◽  
Zahed Siddique ◽  
Pedro Huebner
Keyword(s):  
High Performance ◽  
Disease Diagnosis ◽  
The Novel ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Patient Screening ◽  
Global Pandemic ◽  
Chest X Ray ◽  
Interpretable Model ◽  
Novel Coronavirus

The COVID-19 global pandemic caused by the widespread transmission of the novel coronavirus (SARS-CoV-2) has become one of modern history’s most challenging issues from a healthcare perspective. At its dawn, still without a vaccine, contagion containment strategies remained most effective in preventing the disease’s spread. Patient isolation has been primarily driven by the results of polymerase chain reaction (PCR) testing, but its initial reach was challenged by low availability and high cost, especially in developing countries. As a means of taking advantage of a preexisting infrastructure for respiratory disease diagnosis, researchers have proposed COVID-19 patient screening based on the results of Chest Computerized Tomography (CT) and Chest Radiographs (X-ray). When paired with artificial-intelligence- and deep-learning-based approaches for analysis, early studies have achieved a comparatively high accuracy in diagnosing the disease. Considering the opportunity to further explore these methods, we implement six different Deep Convolutional Neural Networks (Deep CNN) models—VGG16, MobileNetV2, InceptionResNetV2, ResNet50, ResNet101, and VGG19—and use a mixed dataset of CT and X-ray images to classify COVID-19 patients. Preliminary results showed that a modified MobileNetV2 model performs best with an accuracy of 95 ± 1.12% (AUC = 0.816). Notably, a high performance was also observed for the VGG16 model, outperforming several previously proposed models with an accuracy of 98.5 ± 1.19% on the X-ray dataset. Our findings are supported by recent works in the academic literature, which also uphold the higher performance of MobileNetV2 when X-ray, CT, and their mixed datasets are considered. Lastly, we further explain the process of feature extraction using Local Interpretable Model-Agnostic Explanations (LIME), which contributes to a better understanding of what features in CT/X-ray images characterize the onset of COVID-19.

scholarly journals MantisCOVID: Rapid X-Ray Chest Radiograph and Mortality Rate Evaluation With Artificial Intelligence For COVID-19

2020 ◽  
Author(s):  
Yağmur Yaşar ◽  
Berat Tuna Karli ◽  
Cem Çöteli ◽  
Mert Burkay Çöteli
Keyword(s):  
Artificial Intelligence ◽  
Mortality Rate ◽  
Chest Radiograph ◽  
Well Being ◽  
Health Economy ◽  
Diagnostic Tools ◽  
Healthy Population ◽  
The Novel ◽  
X Ray ◽  
Novel Coronavirus

AbstractThe novel coronavirus pandemic has negative impacts over the health, economy and well-being of the global population. This negative effect is growing with the high spreading rate of the virus. The most critical step to prevent the spreading of the virus is pre-screening and early diagnosis of the individuals. This results in quaranteeing the patients not to effect the healthy population. COVID-19 is the name of the disease caused by the novel coronavirus. It has a high infection rate and it is urgent to diagnose many patients as we can to prevent the spread of the virus at the early stage. Rapid diagnostic tools development is urgent to save lives. MantisCOVID is a cloud-based pre-diagnosis tool to be accessed from the internet. This tool delivers a rapid screening test by analyzing the X-ray Chest Radiograph scans via Artificial Intelligence (AI) and it also evaluates the mortality rate of patients with the synthesis of the patient’s history with the machine learning methods. This study reveals the methods used over the platform and evaluation of the algorithms via open datasets.

scholarly journals Automated COVID-19 Detection from Chest X-Ray Images : A High Resolution Network (HRNet) Approach 

2020 ◽  
Author(s):  
Sifat Ahmed ◽  
Tonmoy Hossain ◽  
Oishee Bintey Hoque ◽  
Sujan Sarker ◽  
Sejuti Rahman ◽  
...  
Keyword(s):  
High Resolution ◽  
Well Being ◽  
X Rays ◽  
X Ray ◽  
Non Invasive ◽  
Pcr Diagnosis ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
And Control ◽  
Learning Architectures

Abstract Background/ introduction: The pandemic, originated by novel coronavirus 2019 (COVID-19), continuing its devastating effect on the health, well-being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID-19 in the human body to constraint the exposure and control the spread of the virus. Chest X-Rays are one of the non-invasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time-consuming. Our intensive background studies show that, the works till now are not efficient to produce an unbiased detection result.Method: In this work, we propose an automated COVID-19 classification method, utilizing available COVID and non-COVID X-Ray datasets, along with High Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes.Results: To evaluate the proposed method, several baseline experiments have been performed employing numerous deep learning architectures. With extensive experiment, we got a significant result of 99.26% accuracy, 98.53% sensitivity, and 98.82% specificity with HRNet which surpasses the performances of the existing models.Conclusions: Finally, we conclude that our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X-Ray images into the diagnosis of the disease.

scholarly journals Deep Learning Model for Improving the Characterization of Coronavirus on Chest X-ray Images Using CNN

2020 ◽  
Author(s):  
Olaide N. Oyelade ◽  
Absalom E. Ezugwu
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Data Augmentation ◽  
Knowledge Engineering ◽  
The Novel ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray ◽  
Novel Coronavirus

AbstractThe novel Coronavirus, also known as Covid19, is a pandemic that has weighed heavily on the socio-economic affairs of the world. Although researches into the production of relevant vaccine are being advanced, there is, however, a need for a computational solution to mediate the process of aiding quick detection of the disease. Different computational solutions comprised of natural language processing, knowledge engineering and deep learning have been adopted for this task. However, deep learning solutions have shown interesting performance compared to other methods. This paper therefore aims to advance the application deep learning technique to the problem of characterization and detection of novel coronavirus. The approach adopted in this study proposes a convolutional neural network (CNN) model which is further enhanced using the technique of data augmentation. The motive for the enhancement of the CNN model through the latter technique is to investigate the possibility of further improving the performances of deep learning models in detection of coronavirus. The proposed model is then applied to the COVID-19 X-ray dataset in this study which is the National Institutes of Health (NIH) Chest X-Ray dataset obtained from Kaggle for the purpose of promoting early detection and screening of coronavirus disease. Results obtained showed that our approach achieved a performance of 100% accuracy, recall/precision of 0.85, F-measure of 0.9, and specificity of 1.0. The proposed CNN model and data augmentation solution may be adopted in pre-screening suspected cases of Covid19 to provide support to the use of the well-known RT-PCR testing.

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