scholarly journals On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5702
Author(s):  
Gabriel Iluebe Okolo ◽  
Stamos Katsigiannis ◽  
Turke Althobaiti ◽  
Naeem Ramzan
Keyword(s):  
Classification Accuracy ◽  
Imaging Modality ◽  
Diagnostic Methods ◽  
Network Architectures ◽  
X Rays ◽  
Healthy Individuals ◽  
X Ray ◽  
The World ◽  
Chest X Ray ◽  
Classification Tasks

The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more specifically, chest radiography, is a relatively inexpensive medical imaging modality that can potentially offer a solution for the diagnosis of COVID-19 cases. In this work, we examined eleven deep convolutional neural network architectures for the task of classifying chest X-ray images as belonging to healthy individuals, individuals with COVID-19 or individuals with viral pneumonia. All the examined networks are established architectures that have been proven to be efficient in image classification tasks, and we evaluated three different adjustments to modify the architectures for the task at hand by expanding them with additional layers. The proposed approaches were evaluated for all the examined architectures on a dataset with real chest X-ray images, reaching the highest classification accuracy of 98.04% and the highest F1-score of 98.22% for the best-performing setting.

scholarly journals Pneumonia Detection from Chest X-Rays using Neural Networks

2021 ◽  
Vol 9 (VII) ◽  
pp. 910-913
Author(s):  
Ashish Jain
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Great Success ◽  
X Rays ◽  
X Ray ◽  
Open Set ◽  
The World ◽  
Chest X Ray ◽  
Bacteria And Fungi ◽  
Inflammation Of The Lungs

Pneumonia is one of the most serious diseases which cause the most deaths in the world. Viruses, bacteria, and fungi can cause pneumonia. The infection from spreading to the lungs in the human body. In order to diagnose this infection, a chest x-ray is carried out. The doctor uses X-ray image in order to diagnose or monitor the treatment of states in which inflammation of the lungs. X-rays are also used in the diagnosis of diseases such as emphysema, lung cancer, cancer of the line, and pipe, and tuberculosis (tb). However, a diagnosis of pneumonia requiring medical experts to comment on its presence felt in the chest x-ray. For decades, the auto- diagnosis (CAD) systems have been used for the respiratory disease based on chest X-ray images. Deep learning allows machines can quickly extract and classify objects from a photo. Ilham, with the great success of deep learning, we use a deep learning approach to detection of pneumonia into the work. Convolutional neural network that was developed for this study is the inflammation of the lungs. Supervised learning is ANCHORED to the use of features and functions. In general, the data of 5826 images with the help of one of the Kaggle.com. The CNN training and testing, that is, an open set of data. In the proposed method, the high success rate of accurate classification is achieved.

scholarly journals Automatic COVID-19 Detection from X-Ray images using Ensemble Learning with Convolutional Neural Network

2020 ◽  
Author(s):  
Amit Kumar Das ◽  
Sayantani Ghosh ◽  
Samiruddin Thunder ◽  
Rohit Dutta ◽  
Sachin Agarwal ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Medical Personnel ◽  
Human Beings ◽  
Test Set ◽  
X Ray ◽  
The World ◽  
Chest X Ray ◽  
Set Up

Abstract Covid-19 continues to have catastrophic effects on the lives of human beings throughout the world. To combat this disease it is necessary to screen the affected patients in a fast and inexpensive way. One of the most viable steps towards achieving this goal is through radiological examination, Chest X-Ray being the most easily available and least expensive option. In this paper we have proposed a Deep Convolutional Neural Network based solution which can detect the Covid-19 +ve patients using chest X-Ray images. To test the efficacy of the solution we have used publicly available chest X-ray images of Covid +ve and -ve cases. 538 images of Covid +ve patients and 468 images of Covid -ve patients have been divided into 771 trainable images and 235 testing images. Our solution gave a classification accuracy of 95.7% and sensitivity of 98% in the test set-up. We have developed a GUI application for public use. This application can be used on any computer by any medical personnel to detect Covid +ve patients using Chest X-Ray images within a very few seconds.

scholarly journals Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers

2020 ◽  
Author(s):  
Rahul Kumar ◽  
Ridhi Arora ◽  
Vipul Bansal ◽  
Vinodh J Sahayasheela ◽  
Himanshu Buckchash ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intelligent Systems ◽  
Rapid Development ◽  
Healthcare Systems ◽  
World Health ◽  
X Rays ◽  
X Ray ◽  
Deep Feature ◽  
The World ◽  
Chest X Ray

ABSTRACTAccording to the World Health Organization (WHO), the coronavirus (COVID-19) pandemic is putting even the best healthcare systems across the world under tremendous pressure. The early detection of this type of virus will help in relieving the pressure of the healthcare systems. Chest X-rays has been playing a crucial role in the diagnosis of diseases like Pneumonia. As COVID-19 is a type of influenza, it is possible to diagnose using this imaging technique. With rapid development in the area of Machine Learning (ML) and Deep learning, there had been intelligent systems to classify between Pneumonia and Normal patients. This paper proposes the machine learning-based classification of the extracted deep feature using ResNet152 with COVID-19 and Pneumonia patients on chest X-ray images. SMOTE is used for balancing the imbalanced data points of COVID-19 and Normal patients. This non-invasive and early prediction of novel coronavirus (COVID-19) by analyzing chest X-rays can further be used to predict the spread of the virus in asymptomatic patients. The model is achieving an accuracy of 0.973 on Random Forest and 0.977 using XGBoost predictive classifiers. The establishment of such an approach will be useful to predict the outbreak early, which in turn can aid to control it effectively.

scholarly journals Improved COVID-19 detection using data augmentation Deep Convolution GAN and classifier DenseNet.

2021 ◽  
Author(s):  
Debmitra Ghosh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Training Data ◽  
X Rays ◽  
X Ray ◽  
Chest X Ray ◽  
Synthetic Images

Abstract SARS-CoV-2 or severe acute respiratory syndrome coronavirus 2 is considered to be the cause of Coronavirus (COVID-19) which is a viral disease. The rapid spread of COVID-19 is having a detrimental effect on the global economy and health. A chest X-ray of infected patients can be considered as a crucial step in the battle against COVID-19. On retrospections, it is found that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This sparked the introduction of a variety of deep learning systems and studies which have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Although there are certain shortcomings like deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data but the outbreak is recent, so it is large datasets of radiographic images of the COVID-19 infected patients are not available in such a short time. Here, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing a Deep Convolution Generative Adversarial Network-based model. In addition, we demonstrate that the synthetic images produced from DCGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. Although there are several models available, we chose MobileNet as it is a lightweight deep neural network, with fewer parameters and higher classification accuracy. Here we are using a deep neural network-based model to diagnose COVID-19 infected patients through radiological imaging of 5,859 Chest X-Ray images. We are using a Deep Convolutional Neural Network and a pre-trained model “DenseNet 121” for two new label classes (COVID-19 and Normal). To improve the classification accuracy, in our work we have further reduced the number of network parameters by introducing dense blocks that are proposed in DenseNets into MobileNet. By adding synthetic images produced by DCGAN, the accuracy increased to 97%. Our goal is to use this method to speed up COVID-19 detection and lead to more robust systems of radiology.

scholarly journals Automatically identify COVID-19 and Pneumonia patients using Deep learning in Chest X-rays image

2021 ◽  
Vol 12 (3) ◽  
pp. 011-019
Author(s):  
Haris Uddin Sharif ◽  
Shaamim Udding Ahmed
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Infected Patient ◽  
World Health ◽  
X Rays ◽  
Popular Approach ◽  
X Ray ◽  
The World ◽  
Chest X Ray ◽  
Health Organization

At the end of 2019, a new kind of coronavirus (SARS-CoV-2) suffered worldwide and has become the pandemic coronavirus (COVID-19). The outbreak of this virus let to crisis around the world and kills millions of people globally. On March 2020, WHO (World Health Organization) declared it as pandemic disease. The first symptom of this virus is identical to flue and it destroys the human respiratory system. For the identification of this disease, the first key step is the screening of infected patients. The easiest and most popular approach for screening of the COVID-19 patients is chest X-ray images. In this study, our aim to automatically identify the COVID-19 and Pneumonia patients by the X-ray image of infected patient. To identify COVID19 and Pneumonia disease, the convolution Neural Network was training on publicly available dataset on GitHub and Kaggle. The model showed the 98% and 96% training accuracy for three and four classes respectively. The accuracy scores showed the robustness of both model and efficiently deployment for identification of COVID-19 patients.

Spontaneous Pneumomediastinum: Uncommon Cause of Air Leak in Late Preterm Neonate

2021 ◽  
Vol 35 (2) ◽  
pp. 93-94
Author(s):  
Jyotsna Bhushan ◽  
Shagufta Iqbal ◽  
Abhishek Chopra
Keyword(s):  
Respiratory Distress ◽  
Conservative Management ◽  
Preterm Neonate ◽  
Late Preterm ◽  
Intravenous Fluids ◽  
X Rays ◽  
Blood Gas ◽  
X Ray ◽  
Spontaneous Pneumomediastinum ◽  
Chest X Ray

A clinical case report of spontaneous pneumomediastinum in a late-preterm neonate, chest x-ray showing classical “spinnaker sail sign,” which was managed conservatively and had excellent prognosis on conservative management. Respiratory distress in a preterm neonate is a common clinical finding. Common causes include respiratory distress syndrome, transient tachypnea of the newborn, pneumonia, and pneumothorax. Pneumomediastinum is not very common cause of respiratory distress and more so spontaneous pneumomediastinum. We report here a preterm neonate with spontaneous pneumomediastinum who had excellent clinical recovery with conservative management. A male baby was delivered to G3P1A1 mother at 34 + 6 weeks through caesarean section done due to abruptio placenta. Apgar scores were 8 and 9. Maternal antenatal history was uneventful and there were no risk factors for early onset sepsis. Baby had respiratory distress soon after birth with Silverman score being 2/10. Baby was started on oxygen (O2) by nasal prongs through blender 0.5 l/min, FiO2 25%, and intravenous fluids. Blood gas done was normal. Possibility of transient tachypnea of newborn or mild hyaline membrane disease was kept. Respiratory distress increased at 20 h of life (Silverman score: 5), urgent chest x-ray done revealed “spinnaker sign” suggestive of pneumomediastinum, so baby was shifted to O2 by hood with FiO2 being 70%. Blood gas repeated was normal. Baby was managed conservatively on intravenous fluids and O2 by hood. Baby was gradually weaned off from O2 over next 5 days. As respiratory distress decreased, baby was started on orogastric feed, which baby tolerated well and then was switched to oral feeds. Serial x-rays showed resolution of pneumomediastinum. Baby was discharged on day 7 of life in stable condition on breast feeds and room air.

scholarly journals Accessory pathway analysis using a multimodal deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Nishimori ◽  
Kunihiko Kiuchi ◽  
Kunihiro Nishimura ◽  
Kengo Kusano ◽  
Akihiro Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrophysiological Study ◽  
Accessory Pathway ◽  
Learning Model ◽  
X Rays ◽  
X Ray ◽  
Wpw Syndrome ◽  
Chest X Ray ◽  
Deep Learning Model ◽  
Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

scholarly journals Efficacy of chest X-ray in the diagnosis of COVID-19 pneumonia: comparison with computed tomography through a simplified scoring system designed for triage

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Akın Çinkooğlu ◽  
Selen Bayraktaroğlu ◽  
Naim Ceylan ◽  
Recep Savaş
Keyword(s):  
Computed Tomography ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Scoring System ◽  
Imaging Modality ◽  
Ground Glass Opacity ◽  
Initial Presentation ◽  
X Ray ◽  
Chest X Ray

Abstract Background There is no consensus on the imaging modality to be used in the diagnosis and management of Coronavirus disease 2019 (COVID-19) pneumonia. The purpose of this study was to make a comparison between computed tomography (CT) and chest X-ray (CXR) through a scoring system that can be beneficial to the clinicians in making the triage of patients diagnosed with COVID-19 pneumonia at their initial presentation to the hospital. Results Patients with a negative CXR (30.1%) had significantly lower computed tomography score (CTS) (p < 0.001). Among the lung zones where the only infiltration pattern was ground glass opacity (GGO) on CT images, the ratio of abnormality seen on CXRs was 21.6%. The cut-off value of X-ray score (XRS) to distinguish the patients who needed intensive care at follow-up (n = 12) was 6 (AUC = 0.933, 95% CI = 0.886–0.979, 100% sensitivity, 81% specificity). Conclusions Computed tomography is more effective in the diagnosis of COVID-19 pneumonia at the initial presentation due to the ease detection of GGOs. However, a baseline CXR taken after admission to the hospital can be valuable in predicting patients to be monitored in the intensive care units.

scholarly journals 3D Imaging in Unilateral Primary Pulmonary Hypoplasia in an Adult: A Case Report

2011 ◽  
Vol 2011 ◽  
pp. 1-6
Author(s):  
Aristida Georgescu ◽  
Crinu Nuta ◽  
Simona Bondari
Keyword(s):  
3D Imaging ◽  
Pulmonary Hypoplasia ◽  
Left Lung ◽  
Left Lobe ◽  
X Rays ◽  
X Ray ◽  
Female Smoker ◽  
Multidetector Computer Tomography ◽  
Biological Tests ◽  
Chest X Ray

Unilateral primary pulmonary hypoplasia is rare in adulthood (UPHA); it is characterized by a decreased number of bronchial segmentation and decreased/absent alveolar air space. Classical chest X-ray may be confusing, and the biological tests are unspecific. We present a case of UPHA in a 60-year-old female, smoker, with 3 term normal deliveries, who presented with late recurrent pneumonias and bronchiectasis-type symptomathology, arterial hypertension, and obesity. Chest X-rays revealed opacity in the left lower pulmonary zone, an apparent hypoaerated upper left lobe and left deviation of the mediastinum. Preoperatory multidetector computer tomography (MDCT) presented a small retrocardiac left lung with 5-6 bronchial segmentation range and cystic appearance. After pneumonectomy the gross specimen showed a small lung with multiple bronchiectasis and small cysts, lined by hyperplasic epithelium, surrounded by stromal fibrosclerosis. We concluded that this UPHA occurred in the 4–7 embryonic weeks, and the 3D MDCT reconstructions offered the best noninvasive diagnosis.

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