scholarly journals Chest X-ray image classification for viral pneumonia and Сovid-19 using neural networks

2021 ◽  
Vol 45 (1) ◽  
pp. 149-153
Author(s):  
V.G. Efremtsev ◽  
N.G. Efremtsev ◽  
E.P. Teterin ◽  
P.E. Teterin ◽  
E.S. Bazavluk
Keyword(s):  
Neural Networks ◽  
Predictive Ability ◽  
Diagnostic Methods ◽  
Third Party ◽  
Image Brightness ◽  
Radiographic Images ◽  
X Ray ◽  
Complex Models ◽  
Chest X Ray ◽  
Selection Of

The use of neural networks to detect differences in radiographic images of patients with pneu-monia and COVID-19 is demonstrated. For the optimal selection of resize and neural network ar-chitecture parameters, hyperparameters, and adaptive image brightness adjustment, precision, recall, and f1-score metrics are used. The high values of these metrics of classification quality (> 0.91) strongly indicate a reliable difference between radiographic images of patients with pneumonia and patients with COVID-19, which opens up the possibility of creating a model with good predictive ability without involving ready-to-use complex models and without pre-training on third-party data, which is promising for the development of sensitive and reliable COVID-19 express-diagnostic methods.

scholarly journals Concatenation of Pre-Trained Convolutional Neural Networks for Enhanced COVID-19 Screening Using Transfer Learning Technique

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 103
Author(s):  
Oussama El Gannour ◽  
Soufiane Hamida ◽  
Bouchaib Cherradi ◽  
Mohammed Al-Sarem ◽  
Abdelhadi Raihani ◽  
...  
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Diagnostic Methods ◽  
Medical Community ◽  
Severe Respiratory Distress ◽  
X Ray ◽  
General Evaluation ◽  
Proposed Model ◽  
Chest X Ray

Coronavirus (COVID-19) is the most prevalent coronavirus infection with respiratory symptoms such as fever, cough, dyspnea, pneumonia, and weariness being typical in the early stages. On the other hand, COVID-19 has a direct impact on the circulatory and respiratory systems as it causes a failure to some human organs or severe respiratory distress in extreme circumstances. Early diagnosis of COVID-19 is extremely important for the medical community to limit its spread. For a large number of suspected cases, manual diagnostic methods based on the analysis of chest images are insufficient. Faced with this situation, artificial intelligence (AI) techniques have shown great potential in automatic diagnostic tasks. This paper aims at proposing a fast and precise medical diagnosis support system (MDSS) that can distinguish COVID-19 precisely in chest-X-ray images. This MDSS uses a concatenation technique that aims to combine pre-trained convolutional neural networks (CNN) depend on the transfer learning (TL) technique to build a highly accurate model. The models enable storage and application of knowledge learned from a pre-trained CNN to a new task, viz., COVID-19 case detection. For this purpose, we employed the concatenation method to aggregate the performances of numerous pre-trained models to confirm the reliability of the proposed method for identifying the patients with COVID-19 disease from X-ray images. The proposed system was trialed on a dataset that included four classes: normal, viral-pneumonia, tuberculosis, and COVID-19 cases. Various general evaluation methods were used to evaluate the effectiveness of the proposed model. The first proposed model achieved an accuracy rate of 99.80% while the second model reached an accuracy of 99.71%.

scholarly journals A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Soufiane Hamida ◽  
Oussama El Gannour ◽  
Bouchaib Cherradi ◽  
Abdelhadi Raihani ◽  
Hicham Moujahid ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Support System ◽  
Good Accuracy ◽  
Radiographic Images ◽  
X Ray ◽  
Learning Techniques ◽  
Learning Technique ◽  
Chest X Ray ◽  
The Many ◽  
Selection Of

COVID-19 is an infectious disease-causing flu-like respiratory problem with various symptoms such as cough or fever, which in severe cases can cause pneumonia. The aim of this paper is to develop a rapid and accurate medical diagnosis support system to detect COVID-19 in chest X-ray images using a stacking approach combining transfer learning techniques and KNN algorithm for selection of the best model. In deep learning, we have multiple approaches for building a classification system for analyzing radiographic images. In this work, we used the transfer learning technique. This approach makes it possible to store and use the knowledge acquired from a pretrained convolutional neural network to solve a new problem. To ensure the robustness of the proposed system for diagnosing patients with COVID-19 using X-ray images, we used a machine learning method called the stacking approach to combine the performances of the many transfer learning-based models. The generated model was trained on a dataset containing four classes, namely, COVID-19, tuberculosis, viral pneumonia, and normal cases. The dataset used was collected from a six-source dataset of X-ray images. To evaluate the performance of the proposed system, we used different common evaluation measures. Our proposed system achieves an extremely good accuracy of 99.23% exceeding many previous related studies.

scholarly journals Training Artificial Intelligence Neural Networks to Detect Devices on a Chest X-ray, a Pathway to COVID-19 Testing

2020 ◽  
Vol 112 (5) ◽  
pp. S50
Author(s):  
Zachary Eller ◽  
Michelle Chen ◽  
Jermaine Heath ◽  
Uzma Hussain ◽  
Thomas Obisean ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
X Ray ◽  
Chest X Ray

Przewlekły kaszel jako główna manifestacja chłoniaka Hodgkina – opis przypadku

2019 ◽  
Vol 23 (3) ◽  
Author(s):  
Katarzyna Wójcicka ◽  
Andrzej Pogorzelski
Keyword(s):  
Chronic Cough ◽  
Correct Diagnosis ◽  
Diagnostic Methods ◽  
Radiographic Examination ◽  
Older Children ◽  
X Ray ◽  
Volume Curve ◽  
Flow Volume Curve ◽  
Mediastinal Widening ◽  
Chest X Ray

A cough lasting longer than 4-8 weeks, defined as chronic cough, always requires thorough diagnostic evaluation. In addition to detailed history-taking and physical examination, simple and available diagnostic methods, such as chest x-ray and spirometry, should be performed. They may be helpful tool to establish the underlying cause of cough. Many younger children may have difficulties in performing the forced expiratory maneuvers and fulfilling repeatability criteria for spirometry. The disturbances resulting from insufficient cooperation should be considered in interpratation of the obtained results. The shape of the flow-volume curve, which suggests upper or central airways obstruction, can not be ignored and always requires further investigation for diagnosis of respiratory pathology. The chest x-ray is the most frequently performed radiographic examination in children. Accurate interpretation is essential in reaching a correct diagnosis. Mediastinal widening on the chest x-ray in children can occur due to a large variety of causes. The normal thymus can take on a variety of sizes and shapes and still be considered normal in the first few years of life. In older children mediastinal widening should be differentiated from mediastinal masses. Lymph node enlargement represents a frequent cause, usually as a result of infection or malignancy. The article reports a case of a 12-year-old boy with chronic cough, mediastinal widening on the chest X-ray and abnormal spirometry results, who was finally diagnosed with stage III Hodgkin’s lymphoma.

Entropy estimation of the fragments of chest X-ray images

2021 ◽  
pp. 20-26
Author(s):  
Aleksei Aleksandrovich Rumyantsev ◽  
Farkhad Mansurovich Bikmuratov ◽  
Nikolai Pavlovich Pashin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Binary Classification ◽  
Statistical Characteristics ◽  
Type I ◽  
Deep Convolutional Neural Networks ◽  
Preliminary Calculation ◽  
X Ray ◽  
The Matrix ◽  
Chest X Ray

The subject of this research is medical chest X-ray images. After fundamental pre-processing, the accumulated database of such images can be used for training deep convolutional neural networks that have become one of the most significant innovations in recent years. The trained network carries out preliminary binary classification of the incoming images and serve as an assistant to the radiotherapist. For this purpose, it is necessary to train the neural network to carefully minimize type I and type II errors. Possible approach towards improving the effectiveness of application of neural networks, by the criteria of reducing computational complexity and quality of image classification, is the auxiliary approaches: image pre-processing and preliminary calculation of entropy of the fragments. The article provides the algorithm for X-ray image pre-processing, its fragmentation, and calculation of the entropy of separate fragments. In the course of pre-processing, the region of lungs and spine is selected, which comprises approximately 30-40% of the entire image. Then the image is divided into the matrix of fragments, calculating the entropy of separate fragments in accordance with Shannon’s formula based pm the analysis of individual pixels. Determination of the rate of occurrence of each of the 255 colors allows calculating the total entropy. The use of entropy for detecting pathologies is based on the assumption that its values differ for separate fragments and overall picture of its distribution between the images with the norm and pathologies. The article analyzes the statistical values: standard deviation of error, dispersion. A fully connected neural network is used for determining the patterns in distribution of entropy and its statistical characteristics on various fragments of the chest X-ray image.

Synthesizing Chest X-Ray Pathology for Training Deep Convolutional Neural Networks

2019 ◽  
Vol 38 (5) ◽  
pp. 1197-1206 ◽  
Author(s):  
Hojjat Salehinejad ◽  
Errol Colak ◽  
Tim Dowdell ◽  
Joseph Barfett ◽  
Shahrokh Valaee
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
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 Radiographic aspects of acute community-acquired bacterial pneumonia and pulmonary tuberculosis in children

2021 ◽  
Author(s):  
Rivo Lova Herilanto Rakotomalala ◽  
Harimino Mireille Rakotondravelo ◽  
Andrianina Harivelo Ranivoson ◽  
Annick Lalaina Robinson
Keyword(s):  
Community Acquired Pneumonia ◽  
University Hospital ◽  
Radiographic Images ◽  
X Ray ◽  
Male Predominance ◽  
Mother And Child ◽  
Chest X Ray ◽  
The Right ◽  
The University ◽  
Tuberculosis In Children

Background: The etiological diagnosis of pneumonia is often difficult because of the impossibility of microbiological confirmation most of the time. Therefore, chest X-ray is still essential for a positive diagnosis and etiological orientation. The main objective of our study was to describe the radiographic aspects of acute community-acquired pneumonia and tubercular pneumonia in children.Methods: This was a descriptive retrospective study conducted at the university hospital mother and child of Tsaralalana from January 1st to July 31st, 2017.Results: Sixty-nine cases of pneumonia were included, including 13 cases of TB pneumonia and 46 cases of acute community-acquired pneumonia. The average age was 36.68 months with a male predominance. Clinically, respiratory functional signs predominated in both cases. Alteration in general condition was mainly observed in tubercular pneumonia (26.08%). Alveolar syndromes were present in 43.47% of TB pneumonias and 36.94% of acute community-acquired pneumonia. With regard to the radiographic images, alveolar involvement was common to both types of pneumonia; the nodular image was present in 8.69% of the tubercular pneumonias and 2.17% of the acute community-acquired pneumonia; the cavity image was present only in the tubercular pneumonia (p=0.04); the right-sided location predominated in both cases.Conclusions: X-ray images were common to both TB pneumonia and acute community-acquired pneumonia; some images were specific to TB pneumonia. However, the etiologic orientation of pneumonia is based on a combination of epidemiologic, clinical, and radiographic evidence.

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