scholarly journals Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 651 ◽  
Author(s):  
Mohamed Loey ◽  
Florentin Smarandache ◽  
Nour Eldeen M. Khalifa
Keyword(s):  
Transfer Learning ◽  
Healthcare Systems ◽  
World Health ◽  
Transfer Model ◽  
X Rays ◽  
Computer Algorithms ◽  
X Ray ◽  
The Third ◽  
The World ◽  
Testing Accuracy

The coronavirus (COVID-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

COVID-19 Chest X-Ray Images Diagnosis: A Neutrosophic and Deep Transfer Learning Approach

2020 ◽  
Author(s):  
Nour Eldeen M. Khalifa ◽  
Florentin Smarandache ◽  
Mohamed Loey
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
World Health ◽  
Transfer Model ◽  
X Ray ◽  
Proposed Model ◽  
The World ◽  
Chest X Ray ◽  
Testing Accuracy ◽  
Transfer Models

Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With the advancements in computer science algorithms, the detection of this type of virus in the early stages is urgently needed for the fast recovery of patients. In this paper, a neutrosophic with a deep learning model for the detection of COVID-19 from chest X-ray medical digital images is presented. The proposed model relies on neutrosophic theory by converting the medical images from the grayscale spatial domain to the neutrosophic domain. The neutrosophic domain consists of three types of images and they are, the True (T) images, the Indeterminacy (I) images, and the Falsity (F) images. Using neutrosophic images has positively affected the accuracy of the proposed model. The dataset used in this research has been collected from different sources as there is no benchmark dataset for COVID-19 chest X-ray until the writing of this research. The dataset consists of four classes and they are COVID-19, Normal, Pneumonia bacterial, and Pneumonia virus. After the conversion to the neutrosophic domain, the images are fed into three different deep transfer models and they are Alexnet, Googlenet, and Restnet18. Those models are selected as they have a small number of layers on their architectures and they have been used with related work. To test the performance of the conversion to the neutrosophic domain, four scenarios have been tested. The first scenario is training the deep transfer models with True (T) neutrosophic images only. The second one is training on Indeterminacy (I) neutrosophic images, while the third scenario is training the deep models over the Falsity (F) neutrosophic images. The fourth scenario is training over the combined (T, I, F) neutrosophic images. According to the experimental results, the combined (T, I, F) neutrosophic images achieved the highest accuracy possible for the validation, testing and all performance metrics such Precision, Recall and F1 Score using Resnet18 as a deep transfer model. The proposed model achieved a testing accuracy with 78.70%. Furthermore, the proposed model using neutrosophic and Resnet18 had achieved superior testing accuracy with a related work which achieved 52.80% with the same experimental environmental setup and the same deep learning hyperparameters.

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 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 Design of Accurate Classification of COVID-19 Disease in X-Ray Images Using Deep Learning Approach

2021 ◽  
Vol 2 (2) ◽  
pp. 132-148
Author(s):  
Joy Iong-Zong Chen
Keyword(s):  
Transfer Learning ◽  
Research Work ◽  
Well Being ◽  
Classification Model ◽  
Classification Method ◽  
World Health ◽  
Detection Methods ◽  
X Rays ◽  
X Ray

COVID-19 appears to be having a devastating influence on world health and well-being. Moreover, the COVID-19 confirmed cases have recently increased to over 10 million worldwide. As the number of verified cases increase, it is more important to monitor and classify healthy and infected people in a timely and accurate manner. Many existing detection methods have failed to detect viral patterns. Henceforth, by using COVID-19 thoracic x-rays and the histogram-oriented gradients (HOG) feature extraction methodology; this research work has created an accurate classification method for performing a reliable detection of COVID-19 viral patterns. Further, the proposed classification model provides good results by leveraging accurate classification of COVID-19 disease based on the medical images. Besides, the performance of our proposed CNN classification method for medical imaging has been assessed based on different edge-based neural networks. Whenever there is an increasing number of a class in the training network, the accuracy of tertiary classification with CNN will be decreasing. Moreover, the analysis of 10 fold cross-validation with confusion metrics can also take place in our research work to detect various diseases caused due to lung infection such as Pneumonia corona virus-positive or negative. The proposed CNN model has been trained and tested with a public X-ray dataset, which is recently published for tertiary and normal classification purposes. For the instance transfer learning, the proposed model has achieved 85% accuracy of tertiary classification that includes normal, COVID-19 positive and Pneumonia. The proposed algorithm obtains good classification accuracy during binary classification procedure integrated with the transfer learning method.

The Canadian Light Source — History and scientific prospects

2004 ◽  
Vol 82 (6) ◽  
pp. 1028-1042 ◽  
Author(s):  
G M Bancroft
Keyword(s):  
Light Source ◽  
X Rays ◽  
X Ray ◽  
The Third ◽  
Scientific Disciplines ◽  
The World ◽  
History Of ◽  
Synchrotron Light ◽  
Under Construction ◽  
The University

The Canadian Light Source (CLS) in Saskatoon has been under construction for the last 4 years, and will be producing a number of very intense beams of far-IR, IR, soft and hard X-rays in 2004 for use by several hundred Canadian scientists in chemistry, surface and material science, and a host of other scientific disciplines. The CLS will dramatically enhance the Canadian spectroscopic tradition that Gerhard Herzberg help create. I begin this article (from my 2002 CIC Montreal Medal lecture) with an overview of the history of SR in Canada, beginning in 1972 with the first Canadian synchrotron workshop organized at the University of Western Ontario by Bill McGowan, and attended by Dr. Herzberg. The CLS facility is then described, along with the properties of the first and second set of beamlines to be built at the CLS. These SR beams, in the IR and X-ray regions from the third generation CSL ring, will be competitive in brightness and intensity with the best beamlines in the world for most applications. Finally, some of the present Canadian SR research at foreign SR sources is described across the entire SR spectrum. All known spectroscopic and diffraction experiments are dramatically enhanced with SR; and SR opens up new areas of spectroscopy, microscopy, and spectromicroscopy that cannot be studied with any other source of radiation.Key words: synchrotron light, X-rays, infrared, spectroscopy.

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.

Agile stockpiles an insurance for drug supply

2020 ◽  
pp. 4-17
Author(s):  
Eddy Gilissen ◽  
◽  
Chris Mulligan ◽  
Simon Tottman ◽  
Per Troein ◽  
...  
Keyword(s):  
Public Health ◽  
Public Service ◽  
Healthcare Systems ◽  
Essential Medicines ◽  
World Health ◽  
Catastrophic Event ◽  
Health Organizations ◽  
Safety Stock ◽  
The World ◽  
Additional Burden

Healthcare systems across the world are looking at ways of maintaining the continuity of supply of medicines to patients in times of crisis.Whilst this is not a new phenomenon, the additional burden placed on the supply chain during COVID-19 has meant it has come more into the spotlight. The need to use a stockpile can be caused by an interruption to supply, a rapid and unexpected peak in demand, or when both an interruption to supply and a peak in demand occur simultaneously. The objectives of a stockpile will guide the portfolio breadth and depth to be held. Stockpile objectives are broadly driven either by government requirements to protect public health or by organisations seeking toachieve commercial gain. These drivers are not mutually exclusive as in the case of holding safety stock and Public Service Obligation stock. An Emergency Stockpile is Public Health driven and held in order to supply essential medicines during a signifcant or catastrophic event. Emergency stockpiles can be split into three categories — preparation for imminent event, disease specifc response and general contingency stockpiles. Governments and authorities determine which products and volumes should be held in an emergency stockpile which may be guided by the World Health Organizations (WHO) l ist of essential medicines.

scholarly journals Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6450
Author(s):  
Taimur Hassan ◽  
Muhammad Shafay ◽  
Samet Akçay ◽  
Salman Khan ◽  
Mohammed Bennamoun ◽  
...  
Keyword(s):  
Transfer Learning ◽  
State Of The Art ◽  
Autonomous Systems ◽  
Aviation Security ◽  
Dual Energy ◽  
Average Precision ◽  
X Ray ◽  
Shot Classification ◽  
Object Proposals ◽  
The World

Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic process. Many researchers have developed autonomous systems to recognize baggage threats using security X-ray scans. However, all of these frameworks are vulnerable against screening cluttered and concealed contraband items. Furthermore, to the best of our knowledge, no framework possesses the capacity to recognize baggage threats across multiple scanner specifications without an explicit retraining process. To overcome this, we present a novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats. In addition, the proposed detection framework can be well-generalized to multiple scanner specifications due to its capacity to generate object proposals from the unified tensor maps rather than diversified raw scans. We have rigorously evaluated the proposed tensor-shot detector on the publicly available SIXray and GDXray datasets (containing a cumulative of 1,067,381 grayscale and colored baggage X-ray scans). On the SIXray dataset, the proposed framework achieved a mean average precision (mAP) of 0.6457, and on the GDXray dataset, it achieved the precision and F1 score of 0.9441 and 0.9598, respectively. Furthermore, it outperforms state-of-the-art frameworks by 8.03% in terms of mAP, 1.49% in terms of precision, and 0.573% in terms of F1 on the SIXray and GDXray dataset, respectively.

scholarly journals Impact of nanosystems in Staphylococcus aureus biofilms treatment

2019 ◽  
Author(s):  
Rita M Pinto ◽  
Daniela Lopes-de-Campos ◽  
M Cristina L Martins ◽  
Patrick Van Dijck ◽  
Cláudia Nunes ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Antibiotic Therapy ◽  
Economic Burden ◽  
Surgical Procedure ◽  
Healthcare Systems ◽  
World Health ◽  
Small Particles ◽  
The World ◽  
New Strategy ◽  
Health Organization

ABSTRACT Staphylococcus aureus (S. aureus) is considered by the World Health Organization as a high priority pathogen for which new therapies are needed. This is particularly important for biofilm implant-associated infections once the only available treatment option implies a surgical procedure combined with antibiotic therapy. Consequently, these infections represent an economic burden for Healthcare Systems. A new strategy has emerged to tackle this problem: for small bugs, small particles. Here, we describe how nanotechnology-based systems have been studied to treat S. aureus biofilms. Their features, drawbacks and potentialities to impact the treatment of these infections are highlighted. Furthermore, we also outline biofilm models and assays required for preclinical validation of those nanosystems to smooth the process of clinical translation.

scholarly journals PRESIDENTIAL SYMPOSIUM: EDUCATION NETWORKS: STRENGTHENING GERONTOLOGY AND GERIATRICS THROUGH CONNECTIVITY

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S421-S422
Author(s):  
Judith L Howe ◽  
Kathryn Hyer
Keyword(s):  
Graduate Education ◽  
International Organizations ◽  
Research Policy ◽  
Scientific Meeting ◽  
World Health ◽  
Policy And Practice ◽  
The Third ◽  
The World ◽  
Competency Based ◽  
Health Organization

Abstract The AGHE Presidential Symposium, related to the theme of the annual scientific meeting, underscores the importance of networks, collaborations and partnerships in advancing education in gerontology and geriatrics. AGHE has been at the forefront of many innovative programs since it was founded in 1974, contributing to the growth of the field and the recognition of education as one pillar of the field of gerontology and geriatrics, along with research, policy and practice. This symposium highlights three ongoing initiatives that promote connections and collaborations. The first paper discusses the Age-Friendly University (AFU) network which is made of institutions around the globe who have committed themselves to becoming more age-friendly in their programs and policies. AGHE endorses the AFU principles and invites its members and affiliates to call upon their institutions become part of this pioneering initiative. The AFU initiative is one of several international activities that AGHE, global leaders in education on aging, has engaged in. The second paper describes international networking activities such as collaborations with international organizations including the World Health Organization and connecting international and US students. In the third paper, initiatives to connect disciplines and professions through competency-based education and curricula are discussed. For instance, the Gerontology Competencies for Undergraduate and Graduate Education and the Program of Merit promote competency-based gerontology education across disciplines and professions.

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