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 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 The Effect of Image Resolution on Automated Classification of Chest X-rays

2021 ◽  
Author(s):  
Md Inzamam Ul Haque ◽  
Abhishek K Dubey ◽  
Jacob D Hinkle
Keyword(s):  
High Resolution ◽  
Automated Analysis ◽  
Image Resolution ◽  
Free Text ◽  
X Rays ◽  
Training Time ◽  
X Ray ◽  
Radiology Reports ◽  
Chest X Ray ◽  
High Resolution Images

Deep learning models have received much attention lately for their ability to achieve expert-level performance on the accurate automated analysis of chest X-rays. Although publicly available chest X-ray datasets include high resolution images, most models are trained on reduced size images due to limitations on GPU memory and training time. As compute capability continues to advance, it will become feasible to train large convolutional neural networks on high-resolution images. This study is based on the publicly available MIMIC-CXR-JPG dataset, comprising 377,110 high resolution chest X-ray images, and provided with 14 labels to the corresponding free-text radiology reports. We find, interestingly, that tasks that require a large receptive field are better suited to downscaled input images, and we verify this qualitatively by inspecting effective receptive fields and class activation maps of trained models. Finally, we show that stacking an ensemble across resolutions outperforms each individual learner at all input resolutions while providing interpretable scale weights, suggesting that multi-scale features are crucially important to information extraction from high-resolution chest X-rays.

scholarly journals Detecting Coronavirus from Chest X-rays Using Transfer Learning

COVID ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 403-415
Author(s):  
Abeer Badawi ◽  
Khalid Elgazzar
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Healthcare Providers ◽  
Large Set ◽  
X Rays ◽  
Learning Models ◽  
X Ray ◽  
Chest X Ray ◽  
Multi Class Classification ◽  
Novel Coronavirus

Coronavirus disease (COVID-19) is an illness caused by a novel coronavirus family. One of the practical examinations for COVID-19 is chest radiography. COVID-19 infected patients show abnormalities in chest X-ray images. However, examining the chest X-rays requires a specialist with high experience. Hence, using deep learning techniques in detecting abnormalities in the X-ray images is presented commonly as a potential solution to help diagnose the disease. Numerous research has been reported on COVID-19 chest X-ray classification, but most of the previous studies have been conducted on a small set of COVID-19 X-ray images, which created an imbalanced dataset and affected the performance of the deep learning models. In this paper, we propose several image processing techniques to augment COVID-19 X-ray images to generate a large and diverse dataset to boost the performance of deep learning algorithms in detecting the virus from chest X-rays. We also propose innovative and robust deep learning models, based on DenseNet201, VGG16, and VGG19, to detect COVID-19 from a large set of chest X-ray images. A performance evaluation shows that the proposed models outperform all existing techniques to date. Our models achieved 99.62% on the binary classification and 95.48% on the multi-class classification. Based on these findings, we provide a pathway for researchers to develop enhanced models with a balanced dataset that includes the highest available COVID-19 chest X-ray images. This work is of high interest to healthcare providers, as it helps to better diagnose COVID-19 from chest X-rays in less time with higher accuracy.

scholarly journals Deep Learning for Screening COVID-19 using Chest X-Ray Images

2020 ◽  
Author(s):  
Sanhita Basu ◽  
Sushmita Mitra ◽  
Nilanjan Saha
Keyword(s):  
Deep Learning ◽  
Lung Parenchyma ◽  
Clinical Findings ◽  
Data Sets ◽  
X Rays ◽  
X Ray ◽  
Chest X Ray ◽  
Increasing Demand ◽  
Novel Coronavirus ◽  
Diverse Data

AbstractWith the ever increasing demand for screening millions of prospective “novel coronavirus” or COVID-19 cases, and due to the emergence of high false negatives in the commonly used PCR tests, the necessity for probing an alternative simple screening mechanism of COVID-19 using radiological images (like chest X-Rays) assumes importance. In this scenario, machine learning (ML) and deep learning (DL) offer fast, automated, effective strategies to detect abnormalities and extract key features of the altered lung parenchyma, which may be related to specific signatures of the COVID-19 virus. However, the available COVID-19 datasets are inadequate to train deep neural networks. Therefore, we propose a new concept called domain extension transfer learning (DETL). We employ DETL, with pre-trained deep convolutional neural network, on a related large chest X-Ray dataset that is tuned for classifying between four classes viz. normal, other_disease, pneumonia and Covid — 19. A 5-fold cross validation is performed to estimate the feasibility of using chest X-Rays to diagnose COVID-19. The initial results show promise, with the possibility of replication on bigger and more diverse data sets. The overall accuracy was measured as 95.3% ± 0.02. In order to get an idea about the COVID-19 detection transparency, we employed the concept of Gradient Class Activation Map (Grad-CAM) for detecting the regions where the model paid more attention during the classification. This was found to strongly correlate with clinical findings, as validated by experts.

Non Invasive Failure Analysis of Passive Electronic Devices in Wireless Modules Using X-ray Micro Tomography (MicroCT)

2008 ◽  
Author(s):  
Tyler Pendleton ◽  
Luke Hunter ◽  
S. H. Lau
Keyword(s):  
High Resolution ◽  
Failure Analysis ◽  
Electronic Devices ◽  
Interesting Case ◽  
X Rays ◽  
X Ray ◽  
Case Examples ◽  
Non Invasive ◽  
Novel Approach ◽  
Micro Tomography

Abstract Conventional microCTs or 3D x-ray upgrades from existing 2D x-ray systems have two major drawbacks when they are used for failure analysis of advanced packages: Insufficient resolution to image small (1 to 5 microns) materials and the lack of imaging contrast to visualize cracks, whiskers, and defects within low Z materials. This paper discusses some of the failure analysis (FA) case studies of wireless modules using a high resolution micro x-ray CT (XCT). These examples show the value of high resolution XCT as a novel approach to some common package level defects, including some interesting case examples, where failure mechanisms have been uncovered which could not have been done, using conventional means. The non-invasive FA technique for RF modules technique has been shown to dramatically improve the FA engineers' chances of identifying defects over conventional 2D x-rays and avoid the need for physical and tedious cross sectioning of these devices.

SARS n-CoV2-19 detection from chest x-ray images using deep neural networks

2020 ◽  
Vol 16 (5) ◽  
pp. 419-427 ◽  
Author(s):  
Mohammad Khalid Pandit ◽  
Shoaib Amin Banday
Keyword(s):  
Health Care Professionals ◽  
Low Cost ◽  
Fine Tuning ◽  
Medical Community ◽  
X Rays ◽  
Data Set ◽  
Content Type ◽  
X Ray ◽  
Chest X Ray ◽  
Novel Coronavirus

Purpose Novel coronavirus is fast spreading pathogen worldwide and is threatening billions of lives. SARS n-CoV2 is known to affect the lungs of the COVID-19 positive patients. Chest x-rays are the most widely used imaging technique for clinical diagnosis due to fast imaging time and low cost. The purpose of this study is to use deep learning technique for automatic detection of COVID-19 using chest x-rays. Design/methodology/approach The authors used a data set containing confirmed COVID-19 positive, common bacterial pneumonia and healthy cases (no infection). A collection of 1,428 x-ray images is used in this study. The authors used a pre-trained VGG-16 model for the classification task. Transfer learning with fine-tuning was used in this study to effectively train the network on a relatively small chest x-ray data set. Initial experiments show that the model achieves promising results and can be greatly used to expedite COVID-19 detection. Findings The authors achieved an accuracy of 96% and 92.5% in two and three output class cases, respectively. Based on these findings, the medical community can access using x-ray images as possible diagnostic tool for faster COVID-19 detection to complement the already testing and diagnosis methods. Originality/value The proposed method can be used as initial screening which can help health-care professionals to better treat the COVID patients by timely detecting and screening the presence of disease.

scholarly journals Maturation of T and B Lymphocytes in the Assessment of the Immune Status in COVID-19 Patients

Cells ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2615
Author(s):  
Iwona Kwiecień ◽  
Elżbieta Rutkowska ◽  
Krzysztof Kłos ◽  
Ewa Więsik-Szewczyk ◽  
Karina Jahnz-Różyk ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
T Cells ◽  
T Lymphocytes ◽  
Cd8 T Cells ◽  
X Ray ◽  
Healthy Control ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
And Control ◽  
B And T Lymphocytes

Cell response to novel coronavirus disease 19 (COVID-19) is currently a widely researched topic. The assessment of leukocytes population and the maturation of both B and T lymphocytes may be important in characterizing the immunological profile of COVID-19 patients. The aim of the present study was to evaluate maturation of B and T cells in COVID-19 patients with interstitial lesions on chest X-ray (COVID-19 X-ray (+)), without changes on X-ray (COVID-19 X-ray (−)) and in healthy control. The study group consisted of 23 patients divided on two groups: COVID-19 X-ray (+) n = 14 and COVID-19 X-ray (−) n = 9 and control n = 20. The flow cytometry method was performed. We observed a significantly higher percentage of plasmablasts and lower CD4+ lymphocytes in COVID-19 X-ray (+) patients than in COVID-19 X-ray (−) and control. In the COVID-19 X-ray (+) patients, there was a lower proportion of effector CD4+ T cells, naïve CD8+ T cells and higher central memory CD4+ cells and effector CD8+ T cells than control. The above results showed that the assessment of selected cells of B and T lymphocytes by flow cytometry can distinguish patients with COVID-19 and differentiate patients with and without changes on chest X-ray.

scholarly journals Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1388
Author(s):  
Sampa Misra ◽  
Seungwan Jeon ◽  
Seiyon Lee ◽  
Ravi Managuli ◽  
In-Su Jang ◽  
...  
Keyword(s):  
Standard Test ◽  
Polymerase Chain Reaction Test ◽  
Semantic Features ◽  
X Rays ◽  
X Ray ◽  
Reaction Test ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Novel Coronavirus ◽  
Chain Reaction Test

The 2019 novel coronavirus (COVID-19) has spread rapidly all over the world. The standard test for screening COVID-19 patients is the polymerase chain reaction test. As this method is time consuming, as an alternative, chest X-rays may be considered for quick screening. However, specialization is required to read COVID-19 chest X-ray images as they vary in features. To address this, we present a multi-channel pre-trained ResNet architecture to facilitate the diagnosis of COVID-19 chest X-ray. Three ResNet-based models were retrained to classify X-rays in a one-against-all basis from (a) normal or diseased, (b) pneumonia or non-pneumonia, and (c) COVID-19 or non-COVID19 individuals. Finally, these three models were ensembled and fine-tuned using X-rays from 1579 normal, 4245 pneumonia, and 184 COVID-19 individuals to classify normal, pneumonia, and COVID-19 cases in a one-against-one framework. Our results show that the ensemble model is more accurate than the single model as it extracts more relevant semantic features for each class. The method provides a precision of 94% and a recall of 100%. It could potentially help clinicians in screening patients for COVID-19, thus facilitating immediate triaging and treatment for better outcomes.

scholarly journals Detection of Tuberculosis in Chest X-rays using U-Net Architecture

2019 ◽  
Vol 9 (1) ◽  
pp. 2514-2519
Keyword(s):  
Deep Learning ◽  
False Negative ◽  
Learning System ◽  
X Rays ◽  
X Ray ◽  
Intelligence System ◽  
Chest X Ray ◽  
Learning Architectures ◽  
Aided Diagnosis ◽  
Negative Detection

x-rays are the most commonly performed which are costly diagnostic imaging tests ordered by physicians. Here we are proposing an artificial intelligence system that can reliably separate normal from abnormal would be invaluable in addressing the problem of undiagnosed disease and the lack of radiologists in low-resource settings. The aim of this study is to develop and validate a deep learning system to detect chest x-ray abnormalities and hence detect Tuberculosis (TB) and to provide a tool for Computer Aided Diagnosis (CAD).In this paper by trying to explore existing systems of Image Processing and Deep learning architectures, we are trying to achieve radiologist level detection as well as lower False Negative detection of TB by using ensemble datasets and algorithms. The prototype of a WebApp is created and can be checked on https://parth-patel12.github.io where one can upload the chest x-ray which give probabilities of the chest x-ray to be normal or TB affected.

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.

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