scholarly journals Chest X-ray image analysis and classification for COVID-19 pneumonia detection using Deep CNN

2020 ◽  
Author(s):  
Terry Gao ◽  
Grace Wang
Keyword(s):  
Training Dataset ◽  
X Rays ◽  
X Ray ◽  
Speed Up ◽  
Chest X Ray ◽  
Coronavirus Infection ◽  
Deep Cnn ◽  
Detection And Diagnosis ◽  
The Individual ◽  
New Diagnosis

Abstract To speed up the discovery of COVID-19 disease mechanisms, this research developed a new diagnosis platform using a deep convolutional neural network (CNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients at Middlemore Hospital based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The research idea is that a set of X-ray medical lung images (which include normal, infected by bacteria, infected by virus including COVID-19) were used to train a deep CNN that can distinguish between the noise and the useful information and then uses this training to interpret new images by recognizing patterns that indicate certain diseases such as coronavirus infection in the individual images. The supervised learning method is used as the process of learning from the training dataset and can be thought of as a doctor supervising the learning process. It becomes more accurate as the number of analyzed images grows. In this way, it imitates the training for a doctor, but the theory is that since it is capable of learning from a far larger set of images than any human, it can have the potential of being more accurate.

scholarly journals Chest X-ray image analysis and classification for COVID-19 pneumonia detection using Deep CNN

2020 ◽  
Author(s):  
Terry Gao
Keyword(s):  
Training Dataset ◽  
X Rays ◽  
X Ray ◽  
Average Accuracy ◽  
Speed Up ◽  
Chest X Ray ◽  
Deep Cnn ◽  
Detection And Diagnosis ◽  
The Individual ◽  
New Diagnosis

Abstract To speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (CNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The research idea is that a set of X-ray medical lung images (which include normal, infected by bacteria, infected by virus including COVID-19) are used to train a deep CNN that can distinguish between the noise and the useful information and then uses this training to interpret new images by recognizing patterns that indicate certain diseases such as coronavirus infection in the individual images. The supervised learning method is used as the process of learning from the training dataset and can be thought of as a doctor supervising the learning process. It becomes more accurate as the number of analyzed images grows, and the average accuracy is above 95%. In this way, it imitates the training for a doctor, but the theory is that since it is capable of learning from a far larger set of images than any human, it can have the potential of being more accurate.

scholarly journals Chest X-ray image analysis and classification for COVID-19 pneumonia detection using Deep CNN

2020 ◽  
Author(s):  
Terry Gao
Keyword(s):  
Training Dataset ◽  
X Rays ◽  
X Ray ◽  
Average Accuracy ◽  
Speed Up ◽  
Chest X Ray ◽  
Deep Cnn ◽  
Detection And Diagnosis ◽  
The Individual ◽  
New Diagnosis

AbstractTo speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (CNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The research idea is that a set of X-ray medical lung images (which include normal, infected by bacteria, infected by virus including COVID-19) are used to train a deep CNN that can distinguish between the noise and the useful information and then uses this training to interpret new images by recognizing patterns that indicate certain diseases such as coronavirus infection in the individual images. The supervised learning method is used as the process of learning from the training dataset and can be thought of as a doctor supervising the learning process. It becomes more accurate as the number of analyzed images grows, and the average accuracy is above 95%. In this way, it imitates the training for a doctor, but the theory is that since it is capable of learning from a far larger set of images than any human, it can have the potential of being more accurate.

scholarly journals Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Hou ◽  
Terry Gao
Keyword(s):  
Large Scale ◽  
Rapid Screening ◽  
X Rays ◽  
X Ray ◽  
Disease Mechanisms ◽  
Average Accuracy ◽  
Speed Up ◽  
Chest X Ray ◽  
Detection And Diagnosis ◽  
New Diagnosis

AbstractTo speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (DCNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The explainable method is also used in the DCNN to select instances of the X-ray dataset images to explain the behavior of training-learning models to achieve higher prediction accuracy. The average accuracy of our method is above 96%, which can replace manual reading and has the potential to be applied to large-scale rapid screening of COVID-9 for widely use cases.

scholarly journals One Shot Cluster Based Approach for the Detection of COVID-19 from Chest X-Ray Images

2020 ◽  
Author(s):  
V.N. Manjunath Aradhya ◽  
Mufti Mahmud ◽  
Basant Agarwal ◽  
D.S. Guru ◽  
M. Shamim Kaiser
Keyword(s):  
Virus Disease ◽  
X Rays ◽  
Probabilistic Neural Networks ◽  
X Ray ◽  
The Core ◽  
Wide Range ◽  
Test Instance ◽  
Chest X Ray ◽  
Detection And Diagnosis ◽  
The Way

Corona virus disease (COVID-19) has infected over more than 10 million people around the globe and killed at least 500K worldwide by the end of June 2020. As this disease continues to evolve and scientists and researchers around the world now trying to find out the way to combat this disease in most effective way. Chest X-rays are widely available modality for immediate care in diagnosing COVID-19. Precise detection and diagnosis of COVID-19 from these chest X-rays would be practical for the current situation. This paper proposes one shot cluster based approach for the accurate detection of COVID-19 chest x-rays. The main objective of one shot learning (OSL) is to mimic the way humans learn in order to make classification or prediction on a wide range of similar but novel problems. The core constraint of this type of task is that the algorithm should decide on the class of a test instance after seeing just one test example. For this purpose we have experimented with widely known Generalized Regression and Probabilistic Neural Networks. Experiments conducted with publicly available chest x-ray images demonstrate that the method can detect COVID-19 accurately with high precision. The obtained results have outperformed many of the convolutional neural network based existing methods proposed in the literature.

scholarly journals Effective Survey on Detection and Classification of COVID-19 Suspected Individual Using CT scan Images

2021 ◽  
pp. 294-299
Author(s):  
Snehal R. Sambhe ◽  
Dr. Kamlesh A. Waghmare
Keyword(s):  
Ct Scan ◽  
X Rays ◽  
Rt Pcr ◽  
X Ray ◽  
Feature Extractor ◽  
Chest X Ray ◽  
Coronavirus Infection ◽  
Aided Diagnosis ◽  
Excessive Risk

As insufficient testing kits are available, the development of new testing kits for detecting COVID remains an open vicinity of research. It’s impossible to test each and every patient suffering from coronavirus symptoms using the traditional method i.e. RT-PCR. This test requires more time to produce results and have less sensitivity. Detecting feasible coronavirus infection using chest X-Ray may also assist quarantine excessive risk sufferers while testing results are disclosed. A learning model can be built based on CT scan images or Chest X-rays of individuals with higher accuracy. This paper represents a computer-aided diagnosis of COVID 19 infection bases on a feature extractor by using CNN models.

scholarly journals Problems of Deploying CNN Transfer Learning to Detect COVID-19 from Chest X-rays

2020 ◽  
Author(s):  
Taban Majeed ◽  
Rasber Rashid ◽  
Dashti Ali ◽  
Aras Asaad
Keyword(s):  
Transfer Learning ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Diagnostic Process ◽  
X Rays ◽  
X Ray ◽  
Support Mechanism ◽  
Speed Up ◽  
Chest X Ray ◽  
The Many

AbstractThe Covid-19 first occurs in Wuhan, China in December 2019. After that the virus spread all around the world and at the time of writing this paper the total number of confirmed cases are above 4.7 million with over 315000 deaths. Machine learning algorithms built on radiography images can be used as a decision support mechanism to aid radiologists to speed up the diagnostic process. The aim of this work is to conduct a critical analysis to investigate the applicability of convolutional neural networks (CNNs) for the purpose of COVID-19 detection in chest X-ray images and highlight the issues of using CNN directly on the whole image. To achieve this task, we first use 12-off-the-shelf CNN architectures in transfer learning mode on 3 publicly available chest X-ray databases together with proposing a shallow CNN architecture in which we train it from scratch. Chest X-ray images fed into CNN models without any preprocessing to follow the many of researches using chest X-rays in this manner. Next, a qualitative investigation performed to inspect the decisions made by CNNs using a technique known as class activation maps (CAM). Using CAMs, one can map the activations contributed most to the decision of CNNs back to the original image to visualize the most discriminating regions on the input image.We conclude that CNN decisions should not be taken into consideration, despite their high classification accuracy, until clinicians can visually inspect, and approve, the region(s) of the input image used by CNNs that lead to its prediction.

scholarly journals Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: a first experience from Lombardy, Italy

2020 ◽  
Author(s):  
Isabella Castiglioni ◽  
Davide Ippolito ◽  
Matteo Interlenghi ◽  
Caterina Beatrice Monti ◽  
Christian Salvatore ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Area Under The Curve ◽  
Training Dataset ◽  
List Type ◽  
X Rays ◽  
Independent Dataset ◽  
X Ray ◽  
Chest X Ray

AbstractObjectivesWe tested artificial intelligence (AI) to support the diagnosis of COVID-19 using chest X-ray (CXR). Diagnostic performance was computed for a system trained on CXRs of Italian subjects from two hospitals in Lombardy, Italy.MethodsWe used for training and internal testing an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals. We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as reference standard.ResultsAt 10-fold cross-validation, our AI model classified COVID-19 and non COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74–0.81), 0.82 specificity (95% CI 0.78–0.85) and 0.89 area under the curve (AUC) (95% CI 0.86–0.91). For the independent dataset, AI showed 0.80 sensitivity (95% CI 0.72–0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73–0.87), and 0.81 AUC (95% CI 0.73– 0.87). Radiologists’ reading obtained 0.63 sensitivity (95% CI 0.52–0.74) and 0.78 specificity (95% CI 0.61–0.90) in one centre and 0.64 sensitivity (95% CI 0.52–0.74) and 0.86 specificity (95% CI 0.71–0.95) in the other.ConclusionsThis preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of AI for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.Key pointsArtificial intelligence based on convolutional neural networks was preliminary applied to chest-X-rays of patients suspected to be infected by COVID-19.Convolutional neural networks trained on a limited dataset of 250 COVID-19 and 250 non-COVID-19 were tested on an independent dataset of 110 patients suspected for COVID-19 infection and provided a balanced performance with 0.80 sensitivity and 0.81 specificity.Training on larger multi-institutional datasets may allow this tool to increase its performance.

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 Physical and chemical imaging of adhesive interfaces with soft X-rays

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Hiroyuki Yamane ◽  
Masaki Oura ◽  
Osamu Takahashi ◽  
Tomoko Ishihara ◽  
Noriko Yamazaki ◽  
...  
Keyword(s):  
Molecular Level ◽  
Covalent Bond ◽  
Chemical Imaging ◽  
Diglycidyl Ether ◽  
X Rays ◽  
X Ray ◽  
Covalent Bond Formation ◽  
Chemical States ◽  
The Individual ◽  
Physical And Chemical

AbstractAdhesion is an interfacial phenomenon that is critical for assembling carbon structural composites for next-generation aircraft and automobiles. However, there is limited understanding of adhesion on the molecular level because of the difficulty in revealing the individual bonding factors. Here, using soft X-ray spectromicroscopy we show the physical and chemical states of an adhesive interface composed of a thermosetting polymer of 4,4’-diaminodiphenylsulfone-cured bisphenol A diglycidyl ether adhered to a thermoplastic polymer of plasma-treated polyetheretherketone. We observe multiscale phenomena in the adhesion mechanisms, including sub-mm complex interface structure, sub-μm distribution of the functional groups, and molecular-level covalent-bond formation. These results provide a benchmark for further research to examine how physical and chemical states correlate with adhesion, and demonstrate that soft X-ray imaging is a promising approach for visualizing the physical and chemical states at adhesive interfaces from the sub-mm level to the molecular level.

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