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 Pneumonia Detection Using Convolutional Neural Networks

2021 ◽  
Vol 7 (01) ◽  
pp. 77-80
Author(s):  
Puneet Gupta
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Disease Classification ◽  
World Health ◽  
X Rays ◽  
X Ray ◽  
Life Threatening ◽  
Chest X Ray ◽  
Health Organization

Abstract— Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans commonly caused by bacteria called Streptococcus pneumoniae. One in three deaths in India is caused due to pneumonia as reported by World Health Organization (WHO). Chest X-Rays which are used to diagnose pneumonia, need expert radiotherapists for evaluation. Thus, developing an automatic system for detecting pneumonia would be beneficial for treating the disease without any delay particularly in remote areas. Due to the success of deep learning algorithms in analyzing medical images, Convolutional Neural Networks (CNNs) have gained much attention for disease classification. In addition, features learned by pre-trained CNN models on large-scale datasets are much useful in image classification tasks. In this work, we appraise the functionality of pre-trained CNN models utilized as feature-extractors followed by different classifiers for the classification of abnormal and normal chest X-Rays. We analytically determine the optimal CNN model for the purpose. Statistical results obtained demonstrates that pretrained CNN models employed along with supervised classifier algorithms can be very beneficial in analyzing chest X-ray images, specifically to detect Pneumonia. In this project Transfer learning and a CNN Model is used to detect whether the person has pneumonia or not using chest x-ray.

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.

scholarly journals X-ray image based pneumonia classification using convolutional neural networks

2020 ◽  
Vol 6 (20) ◽  
pp. 54-67
Author(s):  
Sarah Badr AlSumairi ◽  
Mohamed Maher Ben Ismail
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Data Augmentation ◽  
Critical Factor ◽  
Classification Problem ◽  
X Rays ◽  
X Ray ◽  
Erroneous Decision ◽  
Short Period

Pneumonia is an infectious disease of the lungs. About one third to one half of pneumonia cases are caused by bacteria. Early diagnosis is a critical factor for a successful treatment process. Typically, the disease can be diagnosed by a radiologist using chest X-ray images. In fact, chest X-rays are currently the best available method for diagnosing pneumonia. However, the recognition of pneumonia symptoms is a challenging task that relies on the availability of expert radiologists. Such “human” diagnosis can be inaccurate and subjective due to lack of clarity and erroneous decision. Moreover, the error can increase more if the physician is requested to analyze tens of X-rays within a short period of time. Therefore, Computer-Aided Diagnosis (CAD) systems were introduced to support and assist physicians and make their efforts more productive. In this paper, we investigate, design, implement and assess customized Convolutional Neural Networks to overcome the image-based Pneumonia classification problem. Namely, ResNet-50 and DenseNet-161 models were inherited to design customized deep network architecture and improve the overall pneumonia classification accuracy. Moreover, data augmentation was deployed and associated with standard datasets to assess the proposed models. Besides, standard performance measures were used to validate and evaluate the proposed system.

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 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 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.

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.

Research and Standard of Care: Lung Cancer in Romania

2012 ◽  
pp. 437-441
Author(s):  
Tudor E. Ciuleanu
Keyword(s):  
Lung Cancer ◽  
Kinase Inhibitors ◽  
Standard Of Care ◽  
Recruitment Rate ◽  
New Drugs ◽  
Cytotoxic Agents ◽  
World Health ◽  
European Medicines Agency ◽  
Incidence And Mortality ◽  
Health Organization

Overview: In Romania, lung cancer is the most frequent cancer in men and fourth most frequent in women, and its incidence and mortality continue to rise. Recently, firm antitobacco policies were implemented, in agreement with the MPOWER strategies recommended by the World Health Organization (WHO). As of January 2012, the recognized “official” standard of care in lung cancer is still represented by the 2009 edition of the European Society for Medical Oncology (ESMO) guidelines. Cancer treatment is free, as the National Program of Oncology covers the budget for all cytotoxic agents and targeted therapy. However, reimbursement for several expensive drugs such as pemetrexed, erlotinib, and bevacizumab is individually approved by a centralized commission. All new drugs registered in Europe by the European Medicines Agency are concomitantly registered in Romania. However, no new drugs (such as gefitinib) or new indications (such as first-line tyrosine-kinase inhibitors or maintenance treatment) have been accepted for reimbursement since 2008. Clinical research is rapidly growing, and Romanian centers demonstrate a high recruitment rate in pivotal trials, despite initial delays because of a slow approval of the studies by authorities.

Study the Anatomy and Physiology of Body's Urinary System and Fibromyalgia Syndrome (FMS) for the Design an Intelligent Alarm System for Monitoring of FMS

2019 ◽  
Vol 1 (1) ◽  
pp. 72-81
Author(s):  
Hamidreza Shirzadfar ◽  
Narsis Gordoghli
Keyword(s):  
Chronic Pain ◽  
International Association ◽  
Continuous Process ◽  
Well Being ◽  
Chronic Illnesses ◽  
World Health ◽  
Definitive Treatment ◽  
Psychological Consequences ◽  
Medical Problems ◽  
Health Organization

In recent years, chronic medical problems have become increasingly prevalent. Chronic ‎illnesses challenge the view of life as a regular and continuous process, a challenge that has ‎important psychological consequences. The long duration of people suffering from these ‎diseases, the long process of treatment and the fact that there is no proper and definitive ‎treatment for most of these diseases and their associated complications have made chronic ‎diseases a detrimental factor in public health. According to the World Health Organization ‎‎(2006), the prevalence of chronic and non-communicable diseases is increasing in all countries, ‎especially developing countries, so that the major challenge for the health system in the present ‎century, is not living people, but better adapted to chronic illnesses and maintaining their ‎mental and social health and well-being Ed's life-threatening chronic physical illness.‎ Chronic pain is a pain that lasts longer than usual, and according to the criteria of the ‎International Association of Pain, this time is defined as at least 3 months to 6 months. Chronic ‎pain is such that not only faced the sick person whit the pressure of the pain but also with many ‎other pressure that affect different parts of her life. Fibromyalgia is one of the most rheumatologic disorders and one of the most resistant chronic ‎pain syndromes. Fibromyalgia is one of the most common musculoskeletal disorders in adults ‎and chronic pain is one of the most common complaints in this group of patients.

Sign in / Sign up

Export Citation Format

Share Document