scholarly journals Medical Image Classification for Coronavirus Disease (COVID-19) Using Convolutional Neural Networks

2021 ◽  
pp. 2740-2747
Author(s):  
Ehsan Ali Al-Zubaidi ◽  
Maad M. Mijwil
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Medical Image ◽  
Early Stage ◽  
World Health ◽  
Elapsed Time ◽  
The World ◽  
Medical Image Classification ◽  
Serious Disease ◽  
Health Organization

     The coronavirus is a family of viruses that cause different dangerous diseases that lead to death. Two types of this virus have been previously found: SARS-CoV, which causes a severe respiratory syndrome, and MERS-CoV, which causes a respiratory syndrome in the Middle East. The latest coronavirus, originated in the Chinese city of Wuhan, is known as the COVID-19 pandemic. It is a new kind of coronavirus that can harm people and was first discovered in Dec. 2019. According to the statistics of the World Health Organization (WHO), the number of people infected with this serious disease has reached more than seven million people from all over the world. In Iraq, the number of people infected has reached more than twenty-two thousand people until April 2020. In this article, we have applied convolutional neural networks (ConvNets) for the detection of the accuracy of computed tomography (CT) coronavirus images that assist medical staffs in hospitals on categorization chest CT-coronavirus images at an early stage. The ConvNets are able to automatically learn and extract features from the medical image dataset. The objective of this study is to train the GoogleNet ConvNet architecture, using the COVID-CT dataset, to classify 425 CT-coronavirus images. The experimental results show that the validation accuracy of GoogleNet in training the dataset is 82.14% with an elapsed time of 74 minutes and 37 seconds.

scholarly journals Analysis of Separability of COVID-19 and Pneumonia in Chest X-ray Images by Means of Convolutional Neural Networks

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 31
Author(s):  
Joaquim de Moura ◽  
Lucía Ramos ◽  
Plácido L. Vidal ◽  
Jorge Novo ◽  
Marcos Ortega
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Clinical Symptoms ◽  
Early Stage ◽  
World Health ◽  
Complete Analysis ◽  
Screening Process ◽  
X Ray ◽  
Chest X Ray ◽  
Health Organization

The new coronavirus (COVID-19) is a disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). On 11 March 2020, the coronavirus outbreak has been labelled a global pandemic by the World Health Organization. In this context, chest X-ray imaging has become a remarkably powerful tool for the identification of patients with COVID-19 infections at an early stage when clinical symptoms may be unspecific or sparse. In this work, we propose a complete analysis of separability of COVID-19 and pneumonia in chest X-ray images by means of Convolutional Neural Networks. Satisfactory results were obtained that demonstrated the suitability of the proposed system, improving the efficiency of the medical screening process in the healthcare systems.

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 Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Hesham M. Eraqi ◽  
Yehya Abouelnaga ◽  
Mohamed H. Saad ◽  
Mohamed N. Moustafa
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Accidents ◽  
Ad Hoc ◽  
Road Traffic ◽  
Driver Distraction ◽  
World Health ◽  
Skin Segmentation ◽  
Visual Elements ◽  
Health Organization

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad hoc methods are often used. In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically weighted ensemble of convolutional neural networks; we show that a weighted ensemble of classifiers using a genetic algorithm yields a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.

scholarly journals Looking at the Bigger Picture—Considering the Hurdles in the Struggle against Nanoplastic Pollution

Nanomaterials ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2536
Author(s):  
Sophie M. Briffa
Keyword(s):  
Environmental Concern ◽  
Early Stage ◽  
Ecological Impact ◽  
Health Impact ◽  
World Health ◽  
One Dimension ◽  
Environmental Persistence ◽  
The World ◽  
Health Organization ◽  
Changes Over Time

Plastics are considered one of the most serious environmental global concerns as they are ubiquitous and contribute to the build-up of pollution. In August 2020, the BBC reported that scientists found 12–21 million tonnes of tiny plastic fragments floating in the Atlantic Ocean. After release into the environment, plastics from consumer items, such as cosmetics and biomedical products, are subject to degradation and break down into microplastics (<5 mm in diameter) and eventually into nanoplastics (<100 nm in at least one dimension). Given their global abundance and environmental persistence, exposure of humans and animals to these micro- and nano- plastics is unavoidable. “We urgently need to know more about the health impact of microplastics because they are everywhere”, says Dr Maria Neira, Director at the World Health Organization. Nanoplastics are also an emerging environmental concern as little is known about their generation, degradation, transformation, ageing, and transportation. Owing to their small size, nanoplastics can be trapped by filter-feeding organisms and can enter the food chain at an early stage. Therefore, there is a gap in the knowledge that vitally needs to be addressed. This minireview considers how nanoplastic research can be made more quantifiable through traceable and trackable plastic particles and more environmentally realistic by considering the changes over time. It considers how nanoplastic research can use industrially realistic samples and be more impactful by incorporating the ecological impact.

scholarly journals Oral Cancer

2021 ◽  
Author(s):  
Xue Xiao ◽  
Zhou Wang
Keyword(s):  
Oral Cancer ◽  
Early Stage ◽  
Premalignant Lesions ◽  
World Health ◽  
Screening Strategies ◽  
The World ◽  
Advanced Stages ◽  
Health Organization ◽  
Optimized Treatment ◽  
Developed World

Oral cancer is a frequent head and neck cancer in developing countries and some developed world. According to the World Health Organization classification 2017, oral cancer influences the anatomical subsites including buccal mucosa, the anterior two-third of the tongue, lip, palate, vestibule, alveolus, floor of the mouth, and gingivae. A variety of premalignant lesions are related with the development of oral cancer, such as leukoplakia, erythroplakia, et al. The predominant histological type of oral cancer is squamous cell carcinoma (SCC). Tobacco and alcohol consumption are regarded as critical etiological factors. Due to the unspecific symptoms in early stage, the majority are diagnosed in advanced stages. Despite the development of medicine over decades, the mortality rate of oral cancer remains high, indicating the importance of optimized treatment and screening strategies.

scholarly journals Review of medical image recognition technologies to detect melanomas using neural networks

2020 ◽  
Vol 21 (S11) ◽  
Author(s):  
Mila Efimenko ◽  
Alexander Ignatev ◽  
Konstantin Koshechkin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Skin Diseases ◽  
High Specificity ◽  
World Health ◽  
Original Research ◽  
English Only ◽  
Pubmed Database ◽  
The World ◽  
Health Organization

Abstract Background Melanoma is one of the most aggressive types of cancer that has become a world-class problem. According to the World Health Organization estimates, 132,000 cases of the disease and 66,000 deaths from malignant melanoma and other forms of skin cancer are reported annually worldwide (https://apps.who.int/gho/data/?theme=main) and those numbers continue to grow. In our opinion, due to the increasing incidence of the disease, it is necessary to find new, easy to use and sensitive methods for the early diagnosis of melanoma in a large number of people around the world. Over the last decade, neural networks show highly sensitive, specific, and accurate results. Objective This study presents a review of PubMed papers including requests «melanoma neural network» and «melanoma neural network dermatoscopy». We review recent researches and discuss their opportunities acceptable in clinical practice. Methods We searched the PubMed database for systematic reviews and original research papers on the requests «melanoma neural network» and «melanoma neural network dermatoscopy» published in English. Only papers that reported results, progress and outcomes are included in this review. Results We found 11 papers that match our requests that observed convolutional and deep-learning neural networks combined with fuzzy clustering or World Cup Optimization algorithms in analyzing dermatoscopic images. All of them require an ABCD (asymmetry, border, color, and differential structures) algorithm and its derivates (in combination with ABCD algorithm or separately). Also, they require a large dataset of dermatoscopic images and optimized estimation parameters to provide high specificity, accuracy and sensitivity. Conclusions According to the analyzed papers, neural networks show higher specificity, accuracy and sensitivity than dermatologists. Neural networks are able to evaluate features that might be unavailable to the naked human eye. Despite that, we need more datasets to confirm those statements. Nowadays machine learning becomes a helpful tool in early diagnosing skin diseases, especially melanoma.

scholarly journals COVID-19: molecular and serological detection methods

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10180 ◽  
Author(s):  
Ahmed E. Dhamad ◽  
Muna A. Abdal Rhida
Keyword(s):  
Early Detection ◽  
Current Knowledge ◽  
Early Stage ◽  
Diagnostic Methods ◽  
World Health ◽  
Detection Methods ◽  
Gold Standard Method ◽  
Advantages And Disadvantages ◽  
The World ◽  
Health Organization

Since COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was declared as a pandemic disease by the World Health Organization in early 2020, many countries, organizations and companies have tried to find the best way to diagnose the virus and contain its spreading. SARS-CoV-2 is a positive-sense single RNA (+ssRNA) coronavirus and mainly spreads through droplets, respiratory secretions, and direct contact. The early detection of the virus plays a central role in lowering COVID19 incidents and mortality rates. Thus, finding a simple, accurate, cheap and quick detection approach for SARS-CoV-2 at early stage of the viral infection is urgent and at high demand all around the world. The Food and Drug Administration and other health agencies have declared Emergency Use Authorization to develop diagnostic methods for COVID-19 and fulfill the demand. However, not all developed methods are appropriate and selecting a suitable method is challenging. Among all detection methods, rRT-PCR is the gold standard method. Unlike molecular methods, serological methods lack the ability of early detection with low accuracy. In this review, we summarized the current knowledge about COVID-19 detection methods aiming to highlight the advantages and disadvantages of molecular and serological methods.

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