scholarly journals Deep Transfer Learning-Based COVID-19 Prediction Using Chest X-Rays

2021 ◽  
pp. 097206342110504
Author(s):  
Saurabh Kumar ◽  
Shweta Mishra ◽  
Sunil Kumar Singh
Keyword(s):  
Transfer Learning ◽  
World Health ◽  
X Rays ◽  
Performance Study ◽  
Accuracy And Precision ◽  
Learning Techniques ◽  
Proposed Model ◽  
Efficacious Treatment ◽  
Novel Coronavirus ◽  
Health Organization

The novel coronavirus disease (COVID-19) is spreading very rapidly across the globe because of its highly contagious nature and is declared as a pandemic by the World Health Organization (WHO). Scientists are endeavouring to ascertain the drugs for its efficacious treatment. Because, until now, no full-proof drug is available to cure this deadly disease. Therefore, identifying COVID-19 positive people and quarantining them can be an effective solution to control its spread. Many machine learning and deep learning techniques are being used quite effectively to classify positive and negative cases. In this work, a deep transfer learning-based model is proposed to classify the COVID-19 cases using chest X-rays or CT scan images of infected persons. The proposed model is based on the ensembling of DenseNet121 and SqueezeNet1.0, which is named as DeQueezeNet. The model can extract the importance of various influential features from the X-ray images, which are effectively used to classify the COVID-19 cases. The performance study of the proposed model depicts its effectiveness in terms of accuracy and precision. A comparative study has also been done with the recently published works, and it is observed that the performance of the proposed model is significantly better.

scholarly journals Deep Transfer Learning-based COVID-19 prediction using Chest X-rays

2020 ◽  
Author(s):  
Saurabh Kumar ◽  
Shweta Mishra ◽  
Sunil Kumar Singh
Keyword(s):  
Transfer Learning ◽  
World Health ◽  
X Rays ◽  
Performance Study ◽  
Accuracy And Precision ◽  
Learning Techniques ◽  
Proposed Model ◽  
Efficacious Treatment ◽  
Novel Coronavirus ◽  
Health Organization

The novel coronavirus disease (COVID-19) is spreading very rapidly across the globe because of its highly contagious nature, and is declared as a pandemic by world health organization (WHO). Scientists are endeavoring to ascertain the drugs for its efficacious treatment. Because, till now, no full-proof drug is available to cure this deadly disease. Therefore, identifying COVID-19 positive people and to quarantine them, can be an effective solution to control its spread. Many machine learning and deep learning techniques are being used quite effectively to classify positive and negative cases. In this work, a deep transfer learning-based model is proposed to classify the COVID-19 cases using chest X-rays or CT scan images of infected persons. The proposed model is based on the ensembling of DenseNet121 and SqueezeNet1.0, which is named as DeQueezeNet. The model can extract the importance of various influential features from the X-ray images, which are effectively used to classify the COVID-19 cases. The performance study of the proposed model depicts its effectiveness in terms of accuracy and precision. A comparative study has also been done with the recently published works and it is observed the performance of the proposed model is significantly better.

Comparative Analysis of Convolutional Neural Networks Applied in the Detection of Pneumonia Through X-Ray Images of Children

2021 ◽  
Vol 18 (2) ◽  
pp. 4-15
Author(s):  
Luan Oliveira Silva ◽  
◽  
Leandro dos Santos Araújo ◽  
Victor Ferreira Souza ◽  
Raimundo Matos Barros Neto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Standard Of Care ◽  
Classification Problem ◽  
World Health ◽  
X Rays ◽  
Medical Problems ◽  
Learning Techniques ◽  
Incidence And Mortality ◽  
Health Organization

Pneumonia is one of the most common medical problems in clinical practice and is the leading fatal infectious disease worldwide. According to the World Health Organization, pneumonia kills about 2 million children under the age of 5 and is constantly estimated to be the leading cause of infant mortality, killing more children than AIDS, malaria, and measles combined. A key element in the diagnosis is radiographic data, as chest x-rays are routinely obtained as a standard of care and can aid to differentiate the types of pneumonia. However, a rapid radiological interpretation of images is not always available, particularly in places with few resources, where childhood pneumonia has the highest incidence and mortality rates. As an alternative, the application of deep learning techniques for the classification of medical images has grown considerably in recent years. This study presents five implementations of convolutional neural networks (CNNs): ResNet50, VGG-16, InceptionV3, InceptionResNetV2, and ResNeXt50. To support the diagnosis of the disease, these CNNs were applied to solve the classification problem of medical radiographs from people with pneumonia. InceptionResNetV2 obtained the best recall and precision results for the Normal and Pneumonia classes, 93.95% and 97.52% respectively. ResNeXt50 achieved the best precision and f1-score results for the Normal class (94.62% and 94.25% respectively) and the recall and f1-score results for the Pneumonia class (97.80% and 97.65%, respectively).

scholarly journals A Deep Learning Approach to Detect COVID-19 Patients from Chest X-ray Images

AI ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 418-435
Author(s):  
Khandaker Haque ◽  
Ahmed Abdelgawad
Keyword(s):  
Deep Learning ◽  
World Health ◽  
X Rays ◽  
X Ray ◽  
Global Pandemic ◽  
Proposed Model ◽  
Dental Diseases ◽  
Chest X Ray ◽  
Artery Disease ◽  
Health Organization

Deep Learning has improved multi-fold in recent years and it has been playing a great role in image classification which also includes medical imaging. Convolutional Neural Networks (CNNs) have been performing well in detecting many diseases including coronary artery disease, malaria, Alzheimer’s disease, different dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial prospect in detecting COVID-19 patients with medical images like chest X-rays and CTs. Coronavirus or COVID-19 has been declared a global pandemic by the World Health Organization (WHO). As of 8 August 2020, the total COVID-19 confirmed cases are 19.18 M and deaths are 0.716 M worldwide. Detecting Coronavirus positive patients is very important in preventing the spread of this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients from chest X-ray images. Two more CNN models with different number of convolution layers and three other models based on pretrained ResNet50, VGG-16 and VGG-19 are evaluated with comparative analytical analysis. All six models are trained and validated with Dataset 1 and Dataset 2. Dataset 1 has 201 normal and 201 COVID-19 chest X-rays whereas Dataset 2 is comparatively larger with 659 normal and 295 COVID-19 chest X-ray images. The proposed model performs with an accuracy of 98.3% and a precision of 96.72% with Dataset 2. This model gives the Receiver Operating Characteristic (ROC) curve area of 0.983 and F1-score of 98.3 with Dataset 2. Moreover, this work shows a comparative analysis of how change in convolutional layers and increase in dataset affect classifying performances.

Current updates on COVID-19 - A review

2020 ◽  
Vol 10 (1) ◽  
pp. 1-8
Author(s):  
Ghotekar D S ◽  
Vishal N Kushare ◽  
Sagar V Ghotekar
Keyword(s):  
World Health Organization ◽  
Common Cold ◽  
Respiratory Diseases ◽  
Gastrointestinal Diseases ◽  
Hubei Province ◽  
World Health ◽  
The World ◽  
The Common ◽  
Novel Coronavirus ◽  
Health Organization

Coronaviruses are a family of viruses that cause illness such as respiratory diseases or gastrointestinal diseases. Respiratory diseases can range from the common cold to more severe diseases. A novel coronavirus outbreak was first documented in Wuhan, Hubei Province, China in December 2019. The World Health Organization (WHO) has declared the coronavirus disease 2019 (COVID-19) a pandemic. A global coordinated effort is needed to stop the further spread of the virus. A novel coronavirus (nCoV) is a new strain that has not been identified in humans previously. Once scientists determine exactly what coronavirus it is, they give it a name (as in the case of COVID-19, the virus causing it is SARS-CoV-2).

An approach to the understanding of the clinical-etiopathological aspect of COVID-19 (SARS-CoV-2)

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 862-869
Author(s):  
Meena Kumari ◽  
Monika Agrawal ◽  
Rakesh Kumar Singh ◽  
Parameswarappa S Byadgi
Keyword(s):  
World Health ◽  
Apical Surface ◽  
Therapy Assessment ◽  
Differentiated Cells ◽  
The World ◽  
Polarized Epithelia ◽  
Well Differentiated ◽  
Novel Coronavirus ◽  
Health Organization ◽  
Epidemiological Characteristics

Currently, the world is facing a health and socioeconomic crisis caused by the novel coronavirus disease COVID-19. On 11 March 2020, the World Health Organization (WHO) has declared this disease as a pandemic. The condition (COVID-19) is an infectious disorder triggered by a newly discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2. Most of the COVID-19 infected patients will experience mild to moderate respiratory symptoms and recover without any unique therapy. Assessment of the clinical and epidemiological characteristics of SARS-CoV-2 cases suggests the infected patients will not be contagious until the onset of severe symptoms and affects the other organs. Well-differentiated cells of apical airway epithelia communicating with ACE2 were promptly infected to SARS-CoV-2 virus. But the expression of ACE 2 in poorly differentiated epithelia facilitated SARS spike (S) protein-pseudo typed virus entry and it is replicated in polarized epithelia and especially exited via the apical surface. Limiting the transmission of COVID-19 infection & its prevention can be regarded as a hierarchy of controls. In this article, we briefly discuss the most recent advances in respect to aetiology, pathogenesis and clinical progression of the disease COVID-19.

Treatment Options in COVID-19: The Role of Bioavailable Antiviral Ribonucleoside Analog on NHC in vitro

2020 ◽  
Author(s):  
Lara Bittmann
Keyword(s):  
World Health Organization ◽  
Clinical Picture ◽  
Treatment Options ◽  
World Health ◽  
Wuhan City ◽  
The World ◽  
Novel Coronavirus ◽  
Health Organization

On December 31, 2019, WHO was informed of cases of pneumonia of unknown cause in Wuhan City, China. A novel coronavirus was identified as the cause by Chinese authorities on January 7, 2020 and was provisionally named "2019-nCoV". This new Coronavirus causes a clinical picture which has received now the name COVID-19. The virus has spread subsequently worldwide and was explained on the 11th of March, 2020 by the World Health Organization to the pandemic.

Novel Coronavirus 2019 Outbreak: A Global Epidemic

2020 ◽  
Vol 17 (12) ◽  
pp. 1458-1464
Author(s):  
Sweta Kamboj ◽  
Rohit Kamboj ◽  
Shikha Kamboj ◽  
Kumar Guarve ◽  
Rohit Dutt
Keyword(s):  
World Health ◽  
Upper Respiratory Tract ◽  
Global Epidemic ◽  
The Public ◽  
The Government ◽  
Tract Disease ◽  
The 1960S ◽  
Novel Coronavirus ◽  
Health Organization ◽  
The Impact

Background: In the 1960s, the human coronavirus was designated, which is responsible for the upper respiratory tract disease in children. Back in 2003, mainly 5 new coronaviruses were recognized. This study directly pursues to govern knowledge, attitude and practice of viral and droplet infection isolation safeguard among the researchers during the outbreak of the COVID-19. Introduction: Coronavirus is a proteinaceous and infectious pathogen. It is an etiological agent of severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome (MERS). Coronavirus, appeared in China from the seafood and poultry market last year, which has spread in various countries, and has caused several deaths. Methods: The literature data has been taken from different search platforms like PubMed, Science Direct, Embase, Web of Science, who.int portal and complied. Results: Corona virology study will be more advanced and outstanding in recent years. COVID-19 epidemic is a threatening reminder not solely for one country but all over the universe. Conclusion: In this review article, we encapsulated the pathogenesis, geographical spread of coronavirus worldwide, also discussed the perspective of diagnosis, effective treatment, and primary recommendations by the World Health Organization, and guidelines of the government to slow down the impact of the virus are also optimistic, efficacious and obliging for the public health. However, it will take a prolonged time in the future to overcome this epidemic.

A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes

2020 ◽  
Vol 16 ◽  
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Medicine ◽  
Microvascular Complications ◽  
Descriptive Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Public Health Issue ◽  
Learning Techniques ◽  
Health Organization

Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged as a serious public health issue worldwide. According to the World Health Organization (WHO), without interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels and nerves. Method: The paper presents a critical review of existing statistical and Artificial Intelligence (AI) based machine learning techniques with respect to DM complications namely retinopathy, neuropathy and nephropathy. The statistical and machine learning analytic techniques are used to structure the subsequent content review. Result: It has been inferred that statistical analysis can help only in inferential and descriptive analysis whereas, AI based machine learning models can even provide actionable prediction models for faster and accurate diagnose of complications associated with DM. Conclusion: The integration of AI based analytics techniques like machine learning and deep learning in clinical medicine will result in improved disease management through faster disease detection and cost reduction for disease treatment.

An Efficient Approach for Detection and Verification of Infected Cases of Covid-19 Virus through Block Chain: A Survey

2020 ◽  
Vol 10 ◽  
Author(s):  
Kanika Gupta ◽  
Aatif Jamshed
Keyword(s):  
Data Storage ◽  
Operations Management ◽  
World Health ◽  
Chain Model ◽  
Human Beings ◽  
Healthcare Applications ◽  
Food Market ◽  
Block Chain ◽  
Novel Coronavirus ◽  
Health Organization

: Some unknown cases of pneumonia were communicated to World Health Organization (WHO) on 31 December,2019 in China’s Wuhan state. The higher authorities of China informed novel coronavirus as the root cause and labelled as “nCov-2019”. This virus is lying into the virus’s family which propagates the diseases like cold flu, lungs infection and more serious diseases. It is not detected earlier in human beings as it is considered to be a new patch on life. Many countries have increased their surveillance forces around the globe to detect any new novel coronavirus cases. An efficient and safe network for secure data storage i.e. Block chain is used in several applications such as food market, healthcare applications, finance, operations management, Internet of Things (IoT). In this paper, with the use of this emerging technology, are able to track useful information and accelerate the treatment process of patients. It also preserves the person’s identity. Correct implementation of block chain model has the chances to restrict the coronavirus transmissions and its related mortality rate where there are inadequate facilities of testing. Other infectious diseases will also be curbed by this model. The advantages of this model can reach to various stakeholders who are involved in the healthcare field which helps us to restrict the transmission of various diseases.

Critical Insight into the Attributes of Emerging Novel Coronavirus (COVID-19) in India and Across the World

Coronaviruses ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 49-56
Author(s):  
Gaurav M. Doshi ◽  
Hemen S. Ved ◽  
Ami P. Thakkar
Keyword(s):  
Review Article ◽  
World Health ◽  
Effective Vaccine ◽  
Human Contact ◽  
The World ◽  
Critical Insight ◽  
Novel Coronavirus ◽  
Health Organization ◽  
Insight Into ◽  
Old Drugs

The World Health Organization (WHO) has recently announced the spread of novel coronavirus (nCoV) globally and has declared it a pandemic. The probable source of transmission of the virus, which is from animal to human and human to human contact, has been established. As per the statistics reported by the WHO on 11th April 2020, data has shown that more than sixteen lakh confirmed cases have been identified globally. The reported cases related to nCoV in India have been rising substantially. The review article discusses the characteristics of nCoV in detail with the probability of potentially effective old drugs that may inhibit the virus. The research may further emphasize and draw the attention of the world towards the development of an effective vaccine as well as alternative therapies. Moreover, the article will help to bridge the gap between the new researchers since it’s the current thrust area of research.

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