scholarly journals PneumoniaNet: Automated Detection and Classification of Pediatric Pneumonia Using Chest X-ray Images and CNN Approach

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2949
Author(s):  
Roaa Alsharif ◽  
Yazan Al-Issa ◽  
Ali Mohammad Alqudah ◽  
Isam Abu Qasmieh ◽  
Wan Azani Mustafa ◽  
...  
Keyword(s):  
Age Groups ◽  
Area Under The Curve ◽  
Lung Parenchyma ◽  
X Ray ◽  
Pediatric Pneumonia ◽  
Radiological Images ◽  
Chest X Ray ◽  
Prompt Action ◽  
Fold Cross Validation

Pneumonia is an inflammation of the lung parenchyma that is caused by a variety of infectious microorganisms and non-infective agents. All age groups can be affected; however, in most cases, fragile groups are more susceptible than others. Radiological images such as Chest X-ray (CXR) images provide early detection and prompt action, where typical CXR for such a disease is characterized by radiopaque appearance or seemingly solid segment at the affected parts of the lung due to inflammatory exudate formation replacing the air in the alveoli. The early and accurate detection of pneumonia is crucial to avoid fatal ramifications, particularly in children and seniors. In this paper, we propose a novel 50 layers Convolutional Neural Network (CNN)-based architecture that outperforms the state-of-the-art models. The suggested framework is trained using 5852 CXR images and statistically tested using five-fold cross-validation. The model can distinguish between three classes: viz viral, bacterial, and normal; with 99.7% ± 0.2 accuracy, 99.74% ± 0.1 sensitivity, and 0.9812 Area Under the Curve (AUC). The results are promising, and the new architecture can be used to recognize pneumonia early with cost-effectiveness and high accuracy, especially in remote areas that lack proper access to expert radiologists, and therefore, reduces pneumonia-caused mortality rates.

scholarly journals Covidense: Providing a Suitable Solution for Diagnosing Covid-19 Lung Infection Based on Deep Learning from Chest X-Ray Images of Patients

2021 ◽  
Author(s):  
Amir Sorayaie Azar ◽  
Ali Ghafari ◽  
Mohammad Ostadi Najar ◽  
Samin Babaei Rikan ◽  
Reza Ghafari ◽  
...  
Keyword(s):  
Deep Learning ◽  
Bacterial Pneumonia ◽  
Viral Pneumonia ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Detection And Diagnosis ◽  
Fold Cross Validation

Purpose: Coronavirus disease 2019 (Covid-19), first reported in December 2019 in Wuhan, China, has become a pandemic. Chest imaging is used for the diagnosis of Covid-19 patients and can address problems concerning Reverse Transcription-Polymerase Chain Reaction (RT-PCR) shortcomings. Chest X-ray images can act as an appropriate alternative to Computed Tomography (CT) for diagnosing Covid-19. The purpose of this study is to use a Deep Learning method for diagnosing Covid-19 cases using chest X-ray images. Thus, we propose Covidense based on the pre-trained Densenet-201 model and is trained on a dataset comprising chest X-ray images of Covid-19, normal, bacterial pneumonia, and viral pneumonia cases. Materials and Methods: In this study, a total number of 1280 chest X-ray images of Covid-19, normal, bacterial and viral pneumonia cases were collected from open access repositories. Covidense, a convolutional neural network model, is based on the pre-trained DenseNet-201 architecture, and after pre-processing the images, it has been trained and tested on the images using the 5-fold cross-validation method. Results: The accuracy of different classifications including classification of two classes (Covid-19, normal), three classes 1 (Covid-19, normal and bacterial pneumonia), three classes 2 (Covid-19, normal and viral pneumonia), and four classes (Covid-19, normal, bacterial pneumonia and viral pneumonia) are 99.46%, 92.86%, 93.91 %, and 91.01% respectively. Conclusion: This model can differentiate pneumonia caused by Covid-19 from other types of pneumonia, including bacterial and viral. The proposed model offers high accuracy and can be of great help for effective screening. Thus, reducing the rate of infection spread. Also, it can act as a complementary tool for the detection and diagnosis of Covid-19.

scholarly journals Automated Detection of COVID-19 through Convolutional Neural Network using Chest x-ray images

2021 ◽  
Author(s):  
Rubina Sarki ◽  
Khandakar Ahmed ◽  
Hua Wang ◽  
Yanchun Zhang ◽  
Kate Wang
Keyword(s):  
Transfer Learning ◽  
Binary Classification ◽  
Well Being ◽  
X Ray ◽  
Chest X Ray ◽  
Medical Image Classification ◽  
Number Of Classes ◽  
Catastrophic Impact ◽  
Fold Cross Validation

AbstractThe COVID-19 epidemic appears to have a catastrophic impact on global well-being and public health. More than 10 million confirmed cases have been reported worldwide until now. Due to the growing number of confirmed cases, timely and accurate classification of healthy and infected patients is essential to control and treat COVID-19. To this end, in this paper, we aim to develop a deep learning-based system for the persuasive classification and reliable detection of COVID-19 using chest radiography. Firstly, we evaluate the performance of various state-of-the-art convolutional neural networks (CNNs) proposed over recent years for medical image classification. Secondly, we develop and train CNN from scratch. In both cases, we use a recently published public X-Ray dataset for training and validation purposes. For transfer learning, we obtain 100% accuracy for binary classification (i.e., Normal/COVID-19) and 87.50% accuracy for tertiary classification (Normal/COVID-19/Pneumonia). With the CNN trained from scratch, we achieve 93.75% accuracy for tertiary classification. We observe, in the case of transfer learning, the classification accuracy drops with an increased number of classes. Our comprehensive ROC and confusion metric analysis with 10-fold cross-validation strongly underpin our findings.

scholarly journals Estimation of dose and cancer risk to newborn from chest X-ray in South-South Nigeria: a call for protocol optimization

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Akintayo Daniel Omojola ◽  
Michael Onoriode Akpochafor ◽  
Samuel Olaolu Adeneye ◽  
Isiaka Olusola Akala ◽  
Azuka Anthonio Agboje
Keyword(s):  
Cancer Risk ◽  
Age Groups ◽  
Field Size ◽  
Radiological Protection ◽  
International Studies ◽  
X Ray ◽  
Thermoluminescent Dosimeters ◽  
Chest X Ray ◽  
Neonatal Patient ◽  
The Mean

Abstract Background The use of X-ray as a diagnostic tool for complication and anomaly in the neonatal patient has been helpful, but the effect of radiation on newborn stands to increase their cancer risk. This study aims to determine the mean, 50th percentile (quartile 2 (Q2)), and 75th percentile (quartile 3 (Q3)) entrance surface dose (ESD) from anteroposterior (AP) chest X-ray and to compare our findings with other relevant studies. The study used calibrated thermoluminescent dosimeters (TLDs), which was positioned on the central axis of the patient. The encapsulated TLD chips were held to the patients’ body using paper tape. The mean kilovoltage peak (kVp) and milliampere seconds (mAs) used was 56.63(52–60) and 5.7 (5–6.3). The mean background TLD counts were subtracted from the exposed TLD counts and a calibration factor was applied to determine ESD. Results The mean ESDs of the newborn between 1 and 7, 8 and 14, 15 and 21, and 22 and 28 days were 1.09 ± 0.43, 1.15 ± 0.50, 1.19 ± 0.45, and 1.32 ± 0.47 mGy respectively. A one-way ANOVA test shows that there were no differences in the mean doses for the 4 age groups (P = 0.597). The 50th percentile for the 4 age groups was 1.07, 1.26, 1.09, and 1.29 mGy respectively, and 75th percentile were 1.41, 1.55, 1.55, and 1.69 mGy respectively. The mean effective dose (ED) in this study was 0.74 mSv, and the estimated cancer risk was 20.7 × 10−6. Conclusion ESD was primarily affected by the film-focus distance (FFD) and the patient field size. The ESD at 75th percentile and ED in this study was higher compared to other national and international studies. The estimated cancer risk to a newborn was below the International Commission on Radiological Protection (ICRP) limit for fatal childhood cancer (2.8 × 10−2Sv−1).

scholarly journals Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Isabella Castiglioni ◽  
Davide Ippolito ◽  
Matteo Interlenghi ◽  
Caterina Beatrice Monti ◽  
Christian Salvatore ◽  
...  
Keyword(s):  
Deep Learning ◽  
Area Under The Curve ◽  
Clinical Information ◽  
Training Dataset ◽  
Independent Dataset ◽  
X Ray ◽  
Learning Classifier ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Deep Learning Model

Abstract Background We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. Methods We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. Results At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74–0.81), 0.82 specificity (95% CI 0.78–0.85), and 0.89 area under the curve (AUC) (95% CI 0.86–0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72–0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73–0.87), and 0.81 AUC (95% CI 0.73–0.87). Radiologists’ reading obtained 0.63 sensitivity (95% CI 0.52–0.74) and 0.78 specificity (95% CI 0.61–0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52–0.74) and 0.86 specificity (95% CI 0.71–0.95) in Centre 2. Conclusions This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.

scholarly journals 806. 2013–2015 Nationwide Tuberculosis Contact Investigation in Childcare Centers and Schools in Korea

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S289-S289
Author(s):  
Woosuck Suh ◽  
Jong-Hyun Kim ◽  
Ji Hyen Hwang ◽  
Sodam Lee ◽  
Kang-Hee Lee ◽  
...  
Keyword(s):  
School Setting ◽  
Age Groups ◽  
Disease Status ◽  
Childcare Centers ◽  
X Ray ◽  
Contact Investigation ◽  
Chest X Ray ◽  
Latent Tb ◽  
The Republic ◽  
Latent Tb Infection

Abstract Background The Republic of Korea has the highest incidence rate of tuberculosis (TB) among members of the OECD, reported as 78.8/100,000 population in 2016. In response, a state-run intensive contact investigation for TB is being conducted. More effective TB control requires an epidemiologic emphasis on the diagnosis and treatment of latent TB infections in children and adolescents, compared with other age groups. Here we present an analysis of data from the childcare center and school contact investigation by the Korea Centers for Disease Control and Prevention (CDC) in 2013–2015. Methods Data collected from index patients included age, sex, occupation, disease status, results of AFB smear/culture, and chest x-ray. Data collected from contacts included age, sex, results of serial tuberculin skin test (TST), and chest x-ray. Congregate settings included childcare centers, kindergartens, elementary and secondary schools, and age groups were stratified as follows: 0–4 years, 5–12 years, and 13–18 years. TSTs were considered positive if induration ≥10 mm on the first test (TST1) or demonstrated an increase ≥6 mm over the induration of TST1 on repeat testing after 8 weeks (TST2). Results Of the 197,801 subjects with data collected, 173,998 were eligible and included in our analysis. TST1 results were available for 159,346 (91.6%) and when results were positive, induration was 10–14 mm in 7.6% and ≥15 mm in 1.5%. TST2 results were available for 119,797 (82.7%) of the 144,904 with negative TST1, and conversion rate was 9.0%. Altogether considering TST1 and TST2, 17.3% contacts had latent TB infections. Positive rates of TST significantly decreased with age: 20.3% in 0–4 years, 18.8% in 5–12 years, 17.1% in 13–18 years. Conclusion In this 3-year school-setting contact investigation, 17.3% contacts were diagnosed with latent TB infection, as demonstrated by TST reactions. Positive rates of TST significantly but mildly decreased with age. Disclosures All authors: No reported disclosures.

Detection of COVID-19 cases through X-ray images using hybrid deep neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajit Nair ◽  
Santosh Vishwakarma ◽  
Mukesh Soni ◽  
Tejas Patel ◽  
Shubham Joshi
Keyword(s):  
Real Time ◽  
Binary Classification ◽  
Data Set ◽  
Content Type ◽  
X Ray ◽  
Internal Parameters ◽  
Average Accuracy ◽  
Chest X Ray ◽  
Multi Class Classification

Purpose The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud. Design/methodology/approach This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer. Findings The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia. Research limitations/implications One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked. Originality/value Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

scholarly journals Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

2021 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Shoroq Qazan ◽  
Ihssan S. Masad
Keyword(s):  
Artificial Intelligence ◽  
Input Image ◽  
Image Size ◽  
X Ray ◽  
Softmax Classifier ◽  
Non Invasive ◽  
Efficient Detection ◽  
X Ray Imaging ◽  
Chest X Ray

Abstract BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

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