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.

Convolutional Neural Network Based American Sign Language Static Hand Gesture Recognition

2019 ◽  
Vol 10 (3) ◽  
pp. 60-73 ◽  
Author(s):  
Ravinder Ahuja ◽  
Daksh Jain ◽  
Deepanshu Sachdeva ◽  
Archit Garg ◽  
Chirag Rajput
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
American Sign Language ◽  
Sign Language ◽  
Automated System ◽  
World Health ◽  
American Sign ◽  
Hand Gestures ◽  
The World ◽  
Health Organization

Communicating through hand gestures with each other is simply called the language of signs. It is an acceptable language for communication among deaf and dumb people in this society. The society of the deaf and dumb admits a lot of obstacles in day to day life in communicating with their acquaintances. The most recent study done by the World Health Organization reports that very large section (around 360 million folks) present in the world have hearing loss, i.e. 5.3% of the earth's total population. This gives us a need for the invention of an automated system which converts hand gestures into meaningful words and sentences. The Convolutional Neural Network (CNN) is used on 24 hand signals of American Sign Language in order to enhance the ease of communication. OpenCV was used in order to follow up on further execution techniques like image preprocessing. The results demonstrated that CNN has an accuracy of 99.7% utilizing the database found on kaggle.com.

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.

Prediction of infection and death ratio of CoVID-19 virus in Turkey by using artificial neural network (ANN)

Coronaviruses ◽  
2020 ◽  
Vol 01 ◽  
Author(s):  
Andaç Batur Çolak
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Death Rate ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
World Health ◽  
Feed Forward Back Propagation ◽  
The World ◽  
Artificial Neural ◽  
Health Organization

Background: For the first time in December 2019 as reported in the Whuan city of China COVID-19 deadly virus, spread rapidly around the world and the first cases were seen in Turkey on March 11, 2020. On the same day, a pandemic was declared by the World Health Organization due to the rapid spread of the disease throughout the world. Methods: In this study, a multilayered perception feed-forward back propagation neural network has been designed for predicting the spread and mortality rate of COVID-19 virus in Turkey. COVID-19 data from six different countries were used in the design of the artificial neural network, which has 15 neurons in its hidden layer. 70% of these optimized data were used for training, 20% for validation and 10% for testing. Results: The resulting simulation results, COVID-19 virus in Turkey between 20 and 37 days showed the fastest to rise. The number of cases for the 20th day was predicted to be 13.845 and the 51st day for the 37th day. Conclusion: As for the death rate, it was predicted that a rapid rise on the 20th day would start and a slowdown around the 43rd day and progress towards the zero case point. The death rate for the 20th day was predicted to be 170 and the 43rd day for the 1.960s.

scholarly journals Study on Convolutional Neural Network to Detect COVID-19 from Chest X-Rays

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Azher Uddin ◽  
Bayazid Talukder ◽  
Mohammad Monirujjaman Khan ◽  
Atef Zaguia
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Close Contact ◽  
World Health ◽  
Contact Tracing ◽  
X Rays ◽  
Health Crisis ◽  
Gastrointestinal Problems ◽  
The World ◽  
Health Organization

The world is facing a pandemic due to the coronavirus disease 2019 (COVID-19), named as per the World Health Organization. COVID-19 is caused by the virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which was initially discovered in late December 2019 in Wuhan, China. Later, the virus had spread throughout the world within a few months. COVID-19 has become a global health crisis because millions of people worldwide are affected by this fatal virus. Fever, dry cough, and gastrointestinal problems are the most common signs of COVID-19. The disease is highly contagious, and affected people can easily spread the virus to those with whom they have close contact. Thus, contact tracing is a suitable solution to prevent the virus from spreading. The method of identifying all persons with whom a COVID-19-affected patient has come into contact in the last 2 weeks is called contact tracing. This study presents an investigation of a convolutional neural network (CNN), which makes the test faster and more reliable, to detect COVID-19 from chest X-ray (CXR) images. Because there are many studies in this field, the designed model focuses on increasing the accuracy level and uses a transfer learning approach and a custom model. Pretrained deep CNN models, such as VGG16, InceptionV3, MobileNetV2, and ResNet50, have been used for deep feature extraction. The performance measurement in this study was based on classification accuracy. The results of this study indicate that deep learning can recognize SARS-CoV-2 from CXR images. The designed model provided 93% accuracy and 98% validation accuracy, and the pretrained customized models such as MobileNetV2 obtained 97% accuracy, InceptionV3 obtained 98%, and VGG16 obtained 98% accuracy, respectively. Among these models, InceptionV3 has recorded the highest accuracy.

Risk Evaluation in the Emergency Department: An Algorithm for Suicide Prevention

2017 ◽  
Vol 41 (S1) ◽  
pp. S292-S293 ◽  
Author(s):  
T. Duarte ◽  
C. Ferreira ◽  
N. Santos ◽  
D. Sampaio
Keyword(s):  
Risk Factors ◽  
Emergency Room ◽  
World Health Organization ◽  
Suicide Prevention ◽  
Suicide Ideation ◽  
World Health ◽  
Ambulatory Treatment ◽  
Pubmed Database ◽  
The World ◽  
Health Organization

IntroductionSuicide is one of the biggest challenges that psychiatrists face, especially in the emergency room. According to the World Health Organization, there are approximately 3000 suicides every day: one every 40 seconds. About half of all violent deaths in the world are suicides with economic costs of billions of euros. The risk assessment is still based on a subjective approach, with no screening or evaluation tools that support the decision about in-hospital or ambulatory treatment for these patients.ObjectivesCreation of a decision tree algorithm that can be used in the emergency room to guide the clinical decision.AimsIncrease the number of avoided suicides.MethodsPubMed database was searched and articles with the words “emergency”, “suicide”, “attempt” “screening” and “prevention” were included. Articles that used the most reliable and valid measurement tools (i.e., Beck Scale for Suicide Ideation and Suicide Probability Scale) for patient evaluation were selected. World Health Organization guidelines and the Portuguese Suicide Prevention Plan were analyzed and an algorithm was designed based on the major risk factors identified.ResultsNo isolated risk factor was successful for preventing suicide: most are chronic and non-individualized. Having family history of suicide, a mental health disease, a suicide plan and previous suicide attempts are considered major risk factors. The algorithm is based on these factors and takes into account interpersonal variability.ConclusionsThe best way to prevent a suicide is to ask patients for major risk factors, and then, by using this algorithm, treat them accordingly.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals What is Known About Human Milk Bank Donors Around the World: A Systematic Scoping Review

2021 ◽  
pp. 1-37
Author(s):  
Bruna Gutierrez dos Santos ◽  
Maryanne T. Perrin
Keyword(s):  
Human Milk ◽  
Scoping Review ◽  
Maternal Diet ◽  
World Health ◽  
Original Research ◽  
Low Birth Weight Infants ◽  
Milk Bank ◽  
The World ◽  
Health Organization ◽  
The Impact

Abstract Objective: The World Health Organization recommends that low birth weight infants receive donor human milk (DHM) when mother’s milk is not available. Systematic reviews have been published regarding clinical outcomes of infants receiving DHM, as well as the impact of pasteurization on the composition of DHM; however, information about milk bank donors has not been systematically assessed. Design: We conducted a systematic scoping review of original research articles about milk bank donors published before August 2020. Results: A total of 28 studies were included across a variety of geographies: United States (n=8), Brazil (n=7), Spain (n=4), India (n=2), and single studies in France, Norway, Poland, Italy, Taiwan, Korea, and China. Study variables were grouped into 6 main categories: Donor Demographics (n=19), Clinical Characteristics (n=20), Donor Experiences (n=16), Donation Patterns (n=16), Lifestyle Characteristics (n=4), and Lactation/Breastfeeding History (n=8). Some demographic characteristics were commonly reported across regions, while other, including gender and race were infrequently explored. Factors that might influence the composition of DHM, including birth timing (term or preterm), milk type (colostrum, transition or mature), and maternal diet were not regularly studied. Other gaps in the literature included: donors’ motivations and barriers to donation; lactation and breastfeeding history, including factors that influence donors to pump and amass surplus milk; and donation patterns, including whether donors are also selling milk to corporations or sharing milk with peers. Conclusion: What is known about milk bank donors in different geographies is often limited to a single study, with heterogeneity in the variables reported.

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.

scholarly journals Coupling multifunction drones with AI in the fight against the coronavirus pandemic

Computing ◽  
2021 ◽  
Author(s):  
Faris A. Almalki ◽  
Abdullah A. Alotaibi ◽  
Marios C. Angelides
Keyword(s):  
Neural Network ◽  
Close Contact ◽  
Elevation Angle ◽  
Face Mask ◽  
World Health ◽  
Ground Station ◽  
Health Authorities ◽  
The World ◽  
Health Organization ◽  
National Governments

AbstractWhen COVID-19 was declared as a pandemic by the World Health Organization on 11 March 2020, national governments and health authorities across the world begun considering different preventive measures to fight against the coronavirus outbreak. Researchers and tech companies worldwide have been striving to utilize advanced technologies to aid in the fight against the Covid-19 outbreak. This paper aims to couple multifunction drone with AI to deliver wireless services that will help the fight against the Coronavirus pandemic. The proposed drone-eye-system with its thermal imaging cameras and an AI framework utilizes a Convolutional Neural Network (CNN) with its Modified Artificial Neural Network (MANN) for face mask detection of people wearing masks in public. The system can perform basic diagnostic functions such as elevated body temperatures for helping minimize the risk of spreading the infection through close contact. The AI framework evolve an optimized elevation angle $$\uptheta $$ θ and altitude $${\mathrm{h}}_{\mathrm{t}}$$ h t to enhance wireless connectivity between a drone and a ground station, which in turn leads to better throughput and power consumption. The proposed framework has been developed using the MATLAB toolbox and shows promising results with an accuracy of face mask detection of 82.63%, with an F1-score of 0.98, and an enhanced by 10% link budget parameters.

scholarly journals CNN-LSTM Model for Verifying Predictions of Covid-19 Cases

2020 ◽  
pp. 25-32
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay ◽  
Tai-Hoon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
World Health ◽  
Human Beings ◽  
Short Period ◽  
Machine Learning Approach ◽  
The World ◽  
Working Day ◽  
Health Organization ◽  
Near Future

COVID-19 disease came to earth in December 2019 in Wuhan. It is increasing exponentially throughout the world and affected an enormous number of human beings. The World Health Organization (WHO) on March 11, 2020 declared COVID-19 was characterized as “Pandemic”. Clinical Doctors have been working on it 24 hours in the entire world. These doctors are testing whether the particular human has been affected with the disease using testing kit and other related process. Researchers have been working day-night for developing vaccine for the disease. Since the rate of affected people is so high, it is difficult for clinical doctors to check such a large number of coronavirus detected humans within reasonable time. This paper attempts to use Machine Learning Approach to build up model which will help clinical doctors for verification of disease within short period of time and also the paper attempts to predict growth of the disease in near future in the world. Two models were used for achieving this purpose- One is based on Convolutional Neural Network model where as another one consists of Convolutional Neural Network and Recurrent Neural Network. These two models are evaluated and compared for verifying the predicted result with respect to the original one. Experimental results indicate that the combined CNN-LSTM approach outperforms well over the other model.

scholarly journals Fully-Connected Neural Network for COVID-19 Chest X-Ray Imaging Classification Using Hybrid Features

2020 ◽  
Author(s):  
Victor Hugo Viveiros ◽  
Rayanne Lima ◽  
Fernando Lucas Martins ◽  
Alessandra Coelho ◽  
Matheus Baffa
Keyword(s):  
Neural Network ◽  
The Other ◽  
World Health ◽  
Hybrid Features ◽  
X Ray ◽  
Validation Experiment ◽  
The World ◽  
Contact Tracking ◽  
Chest X Ray ◽  
Health Organization

Discovered on 31st December of 2019, the new Coronavirus has a high transmission capacity and was considered pandemic by the World Health Organization. In only six months is was able to spread all over the world and cause more than 600 thousand deaths. Early diagnosis is essential for governments to take public policies, such as social isolation, commerce control, and contact tracking. In order to make these actions, massive tests are required. On the other hand, diagnosis kits are expensive and not accessible to everyone. Medical imaging, such as thoracic x-ray and Computational Tomography (CT) has been used to visualize the lung and to verify at the first moment the presence of viral pneumonia. However, some countries have few radiologists specializing in chest x-ray analysis. The findings in the image are generally not so easy to see and can easily be confused with traditional pneumonia findings. For this reason, studies in Computer Vision are necessary, both to detect anomalies in imaging and to differentiate the other types of pneumonia. This paper addresses the initial results of a research, which developed an image classification methodology to differentiate x-ray images from sick patients, infected with Coronavirus, and healthy patients. The proposed method, based on the extraction and detection of patterns in texture and color features through a Deep Neural Network, obtained an average accuracy of 95% following a k-fold cross-validation experiment.

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