scholarly journals Classification Of X-ray Images For Detecting Covid-19 Using Deep Transfer Learning

2020 ◽  
Author(s):  
Nidhi Bansal ◽  
S.Srid
Keyword(s):  
Transfer Learning ◽  
Network Models ◽  
World Health ◽  
Learning Potential ◽  
Neural Network Models ◽  
X Ray ◽  
Learning Framework ◽  
Health Organization ◽  
High Level

Abstract The coronavirus disease COVID-19 eruption is stated as a pandemic by the World Health Organization. It is affecting around 212 countries and territories across the globe. There is a need to constantly analyze and find patterns from lungs X-Ray images. Early diagnosis can constraint the exposure of person and aids to bound the feast of the virus. The manual diagnosis is quite tedious and time-consuming process. The main aim of this paper is to explore the transfer learning potential. A deep learning framework is proposed adopting the capability of pretrained Deep Convolutional Neural Network models with transfer learning. This assists in classification of the chest X-Ray Images with high level of accuracy. An analysis is done with utilization of six pretrained models – VGG16, VGG19, ResNet50V2, InceptionV3, Xception and NASNetLarge. The experiment results showed that the highest accuracy obtained was 97% using VGG16 and VGG19 with sensitivity and specificity of 100% and 94% respectively.

scholarly journals Pneumonia Detection using Chest X-Ray Images

2021 ◽  
Vol 9 (VI) ◽  
pp. 1611-1618
Author(s):  
Shruti Meshram
Keyword(s):  
Medical Center ◽  
Network Models ◽  
Ensemble Model ◽  
X Rays ◽  
Neural Network Models ◽  
X Ray ◽  
Learning Framework ◽  
Unseen Data ◽  
Chest X Ray ◽  
Bacteria And Fungi

Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pre-trained on Image Net, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pre-trained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children’s Medical Center dataset.

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.

scholarly journals APPLICATION OF NEURAL NETWORKS TO DETERMINE THE COORDINATES OF THE SEAT OF FIRE BY MULTIPOINT ELECTRO-OPTICAL SYSTEM

2021 ◽  
pp. 248-251
Author(s):  
Д.Ф. Пирова ◽  
Б.Э. Забержинский ◽  
А.Г. Золин
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Performance ◽  
Network Models ◽  
X Rays ◽  
Neural Network Models ◽  
X Ray ◽  
Python Programming Language ◽  
Python Programming

Статья посвящена исследованию методов проектирования интеллектуальных информационных систем и применение моделей искусственных нейронных сетей для диагностического прогнозирования развития пневмонии посредством анализа рентгеновских снимков. В этой работе основное внимание уделяется классификации пневмонии и туберкулеза - двух основных заболеваний грудной клетки - на основе рентгеновских снимков грудной клетки. Данное исследование проводилось при помощи открытой нейросетевой библиотеки Keras и языка программирования Python. Система дает пользователю заключение о том, болен он или нет, тем самым помогая врачам и медицинскому персоналу принять быстрое и информированное решение о наличии заболевания. Разработанная модель, может определить, является ли рентгеновский снимок нормальным или имеет отклонения, которые могут быть пневмонией с точностью 94,87%. Полученные результаты указывают на высокую эффективность применения нейронных сетей при диагностировании пневмонии по рентгеновским снимкам. This paper is devoted to the study of methods of designing intellectual information systems and neural network models application on diagnostic prediction of pneumonia development by X-ray images analysis. This article focuses on the classification of pneumonia and tuberculosis - the two main chest diseases - based on chest x-rays. This study was carried out using the Keras open neural network library and the Python programming language. System returns user a conclusion whether the patient is ill or not helping medical staff to make a quick and informed decision about the presence of the disease. The developed model can determine is the X-ray image normal or has anomalies that can be pneumonia with accuracy up to 94.87%. The results obtained indicate the high performance of the applying neural networks in the diagnosis of pneumonia by X-ray images.

scholarly journals Classification of COVID-19 and Pneumonia Using Deep Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Mainuzzaman Mahin ◽  
Sajid Tonmoy ◽  
Rufaed Islam ◽  
Tahia Tazin ◽  
Mohammad Monirujjaman Khan ◽  
...  
Keyword(s):  
Transfer Learning ◽  
World Health ◽  
Health Concern ◽  
Global Pandemic ◽  
Custom Made ◽  
The World ◽  
Learning Technique ◽  
Life Threatening ◽  
Health Organization

The World Health Organization (WHO) recognized COVID-19 as the cause of a global pandemic in 2019. COVID-19 is caused by SARS-CoV-2, which was identified in China in late December 2019 and is indeed referred to as the severe acute respiratory syndrome coronavirus-2. The whole globe was hit within several months. As millions of individuals around the world are infected with COVID-19, it has become a global health concern. The disease is usually contagious, and those who are infected can quickly pass it on to others with whom they come into contact. As a result, monitoring is an effective way to stop the virus from spreading further. Another disease caused by a virus similar to COVID-19 is pneumonia. The severity of pneumonia can range from minor to life-threatening. This is particularly hazardous for children, people over 65 years of age, and those with health problems or immune systems that are affected. In this paper, we have classified COVID-19 and pneumonia using deep transfer learning. Because there has been extensive research on this subject, the developed method concentrates on boosting precision and employs a transfer learning technique as well as a model that is custom-made. Different pretrained deep convolutional neural network (CNN) models were used to extract deep features. The classification accuracy was used to measure performance to a great extent. According to the findings of this study, deep transfer learning can detect COVID-19 and pneumonia from CXR images. Pretrained customized models such as MobileNetV2 had a 98% accuracy, InceptionV3 had a 96.92% accuracy, EffNet threshold had a 94.95% accuracy, and VGG19 had a 92.82% accuracy. MobileNetV2 has the best accuracy of all of these models.

Socio-economic determinants of health

2019 ◽  
Vol 5 (3) ◽  
pp. 42-59 ◽  
Author(s):  
Daria Yu. Moiseeva ◽  
Irina A. Troitskaya
Keyword(s):  
Social Determinants ◽  
Well Being ◽  
Determinants Of Health ◽  
World Health ◽  
Demographic Development ◽  
Economic Determinants ◽  
Health Organization ◽  
High Level ◽  
Interdisciplinary Study

The high level of economic and demographic development of a country does not guarantee health equality to its citizens. Health differentiation is associated not only with genetic, behavioral, or infrastructural factors, despite their significance; it reflects also the socio-economic differentiation of society. Thus, the concept of social determinants of health arises — the of people from their birth to the old age. The World Health Organization defines health as a state of complete physical, mental and social well-being, but not only as the absence of disease and physical defects. This definition, however, does not allow emphasizing that set of objective indicators through which one can monitor and measure health. At present, there is no unique comparable scale for determining the level of health; its development requires a complex interdisciplinary study. This article studies the concept and defines the classification of socio-economic determinants of the population’s health. Using a variety of contemporary foreign studies linking specific indicators of health or life expectancy with a number of social determinants, the current paper reveals the nature of the socio-economic factors influence on the health of the population. Due to the lack of Russian research on the topic, this article is of interest to the Russian audience with a broad overview of theoretical and empirical works.

scholarly journals A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images

2020 ◽  
Vol 10 (2) ◽  
pp. 559 ◽  
Author(s):  
Vikash Chouhan ◽  
Sanjay Kumar Singh ◽  
Aditya Khamparia ◽  
Deepak Gupta ◽  
Prayag Tiwari ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Medical Center ◽  
Network Models ◽  
Ensemble Model ◽  
X Rays ◽  
Neural Network Models ◽  
Learning Framework ◽  
Unseen Data ◽  
Chest X Ray ◽  
Bacteria And Fungi

Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children’s Medical Center dataset.

scholarly journals Automatic Microscopy Analysis with Transfer Learning for Classification of Human Sperm

2021 ◽  
Vol 11 (12) ◽  
pp. 5369
Author(s):  
Rui Liu ◽  
Mingmei Wang ◽  
Min Wang ◽  
Jianqin Yin ◽  
Yixuan Yuan ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Sperm Morphology ◽  
Human Sperm ◽  
World Health ◽  
Sperm Analysis ◽  
Human Fertility ◽  
Average Accuracy ◽  
Microscopy Analysis ◽  
Health Organization

Infertility is a global problem that affects many couples. Sperm analysis plays an essential role in the clinical diagnosis of human fertility. The examination of sperm morphology is an essential technique because sperm morphology is a proven indicator of biological functions. At present, the morphological classification of human sperm is conducted manually by medical experts. However, manual classification is laborious and highly dependent on the experience and capability of clinicians. To address these limitations, we propose a transfer learning method based on AlexNet to automatically classify the sperms into four different categories in terms of the World Health Organization (WHO) standards by analyzing their morphology. We adopt the feature extraction architecture of AlexNet as well as its pre-training parameters. Besides, we redesign the classification network by adding the Batch Normalization layers to improve the performance. The proposed method achieves an average accuracy of 96.0% and an average precision of 96.4% in the freely-available HuSHeM dataset, which exceeds the performance of previous algorithms. Our method shows that automatic sperm classification has great potential to replace manual sperm classification in the future.

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