scholarly journals Deep Learning Models for Pneumonia Identification and Classification Based on X-Ray Images

2021 ◽  
Vol 38 (3) ◽  
pp. 903-909
Author(s):  
Veeranjaneyulu Naralasetti ◽  
Reshmi Khadherbhi Shaik ◽  
Gayatri Katepalli ◽  
Jyostna Devi Bodapati
Keyword(s):  
Experimental Studies ◽  
Cross Entropy ◽  
X Rays ◽  
Weight Decay ◽  
X Ray ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Classification Tasks ◽  
Multi Class Classification ◽  
Fully Connected

Diagnosis based on chest X-rays is widely used and approved for the diagnosis of various diseases such as Pneumonia. Manually screening of theses X-ray images technician or radiologist involves expertise and time consuming. Addressing this, we propose an automated approach for the diagnosis of pneumonia by assisting doctors in spotting infected areas in the X-ray images. We propose a deep Convolutional Neural Network (CNN) model for efficiently detecting the presence of pneumonia in the X-ray images. The proposed CNN is designed with 5 convolution blocks followed by 4 fully connected layers. In order to boost the performance of the model, we incorporate batch normalization, dynamic dropout, learning rate decay, L2 regularization weight decay along with Adam optimizer and binary Cross-Entropy loss function while training the model using back propagating algorithm. The proposed model is validated on two publicly accessible benchmark datasets, and the experimental studies conducted on these datasets indicate that the proposed model is efficient. The suggested CNN architecture with specified hyper parameters allows the model to outperform several existing models by achieving accuracy of 97.73% and 91.17% respectively for binary and multi-class classification tasks of pneumonia disease.

scholarly journals AI_COVID: AUTOMATIC DIAGNOSIS OF COVID-19 USING FRONTAL CHEST X-RAY IMAGE

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 57-63
Author(s):  
I. Allaouzi ◽  
B. Benamrou ◽  
A. Allaouzi ◽  
M. Ouardouz ◽  
M. Ben Ahmed
Keyword(s):  
High Performance ◽  
Binary Classification ◽  
Svm Classifier ◽  
X Rays ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray ◽  
Multi Class Classification ◽  
Performance Results ◽  
Total Accuracy

Abstract. With the continued growth of confirmed cases of COVID-19, a highly infectious disease caused by a newly discovered coronavirus called Severe Acute Respiratory Syndrome Coronavirus 2, or SARS-CoV-2, there is an urgent need to find ways to help clinicians fight the virus by reducing the workload and speeding up the diagnosis of COVID-19. In this work, we propose an artificial intelligence solution “AI_COVID” which can help radiologists to know if the lungs are infected with the virus in just a few seconds.AI_COVID is based on a pre-trained DenseNet-121 model that detects subtle changes in the lungs and an SVM classifier that decides whether these changes are caused by COVID-19 or other diseases. AI_COVID is trained on thousands of frontal chest x-rays of people who have contracted COVID-19, healthy people, and people with viral or bacterial pneumonia. The experimental study is tested on 781 chest x-rays from two publicly available chest x-ray datasets COVID-19 radiography database and COVIDx Dataset. The performance results showed that our proposed model (DenseNet-121 + SVM) demonstrated high performance and yielded excellent results compared to the current methods in the literature, with a total accuracy of 99.74% and 98.85% for binary classification (COVID-19 vs. No COVID-19) and multi-class classification (COVID-19 vs. Normal vs. Pneumonia), respectively.

scholarly journals Truncated Inception Net: COVID-19 Outbreak Screening using Chest X-rays

2020 ◽  
Author(s):  
Dipayan Das ◽  
KC Santosh ◽  
Umapada Pal
Keyword(s):  
Neural Network ◽  
World Wide ◽  
Imaging Techniques ◽  
Imaging Systems ◽  
Ct Scans ◽  
X Rays ◽  
X Ray ◽  
X Ray Imaging ◽  
Proposed Model ◽  
Different Types

Abstract Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in less than a couple of months, and the infection, caused by SARS-CoV-2, is spreading at an unprecedented rate. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs). In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases. To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account. The proposed model achieved an accuracy of 99.96% (AUC of 1.0) in classifying COVID- 19 positive cases from combined Pneumonia and healthy cases. Similarly, it achieved an accuracy of 99.92% (AUC of 0.99) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis and healthy CXRs. To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using CXRs.

An Efficient Method for Coronavirus Detection Through X-rays using deep Neural Network

2021 ◽  
Vol 17 ◽  
Author(s):  
P. Srinivasa Rao ◽  
Pradeep Bheemavarapu ◽  
P. S. Latha Kalyampudi ◽  
T. V. Madhusudhana Rao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Chronic Respiratory Disease ◽  
Automated System ◽  
X Rays ◽  
Medical Problems ◽  
X Ray ◽  
Chest X Ray ◽  
Fully Connected

Background: Coronavirus (COVID-19) is a group of infectious diseases caused by related viruses called coronaviruses. In humans, the seriousness of infection caused by a coronavirus in the respiratory tract can vary from mild to lethal. A serious illness can be developed in old people and those with underlying medical problems like diabetes, cardiovascular disease, cancer, and chronic respiratory disease. For the diagnosis of the coronavirus disease, due to the growing number of cases, a limited number of test kits for COVID-19 are available in the hospitals. Hence, it is important to implement an automated system as an immediate alternative diagnostic option to pause the spread of COVID-19 in the population. Objective: This paper proposes a deep learning model for classification of coronavirus infected patient detection using chest X-ray radiographs. Methods: A fully connected convolutional neural network model is developed to classify healthy and diseased X-ray radiographs. The proposed neural network model consists of seven convolutional layers with rectified linear unit, softmax (last layer) activation functions and max pooling layers which were trained using the publicly available COVID-19 dataset. Results and Conclusion: For validation of the proposed model, the publicly available chest X-ray radiograph dataset consisting COVID-19 and normal patient’s images were used. Considering the performance of the results that are evaluated based on various evaluation metrics such as precision, recall, MSE, RMSE & accuracy, it is seen that the accuracy of the proposed CNN model is 98.07%.

scholarly journals Detecting Coronavirus from Chest X-rays Using Transfer Learning

COVID ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 403-415
Author(s):  
Abeer Badawi ◽  
Khalid Elgazzar
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Healthcare Providers ◽  
Large Set ◽  
X Rays ◽  
Learning Models ◽  
X Ray ◽  
Chest X Ray ◽  
Multi Class Classification ◽  
Novel Coronavirus

Coronavirus disease (COVID-19) is an illness caused by a novel coronavirus family. One of the practical examinations for COVID-19 is chest radiography. COVID-19 infected patients show abnormalities in chest X-ray images. However, examining the chest X-rays requires a specialist with high experience. Hence, using deep learning techniques in detecting abnormalities in the X-ray images is presented commonly as a potential solution to help diagnose the disease. Numerous research has been reported on COVID-19 chest X-ray classification, but most of the previous studies have been conducted on a small set of COVID-19 X-ray images, which created an imbalanced dataset and affected the performance of the deep learning models. In this paper, we propose several image processing techniques to augment COVID-19 X-ray images to generate a large and diverse dataset to boost the performance of deep learning algorithms in detecting the virus from chest X-rays. We also propose innovative and robust deep learning models, based on DenseNet201, VGG16, and VGG19, to detect COVID-19 from a large set of chest X-ray images. A performance evaluation shows that the proposed models outperform all existing techniques to date. Our models achieved 99.62% on the binary classification and 95.48% on the multi-class classification. Based on these findings, we provide a pathway for researchers to develop enhanced models with a balanced dataset that includes the highest available COVID-19 chest X-ray images. This work is of high interest to healthcare providers, as it helps to better diagnose COVID-19 from chest X-rays in less time with higher accuracy.

scholarly journals mAML: an automated machine learning pipeline with a microbiome repository for human disease classification

2020 ◽  
Author(s):  
Fenglong Yang ◽  
Quan Zou
Keyword(s):  
Machine Learning ◽  
Human Disease ◽  
High Performance ◽  
Model Building ◽  
Disease Classification ◽  
Benchmark Datasets ◽  
Automated Machine Learning ◽  
Classification Tasks ◽  
Interpretable Models ◽  
Multi Class Classification

AbstractDue to the concerted efforts to utilize the microbial features to improve disease prediction capabilities, automated machine learning (AutoML) systems designed to get rid of the tediousness in manually performing ML tasks are in great demand. Here we developed mAML, an ML model-building pipeline, which can automatically and rapidly generate optimized and interpretable models for personalized microbial classification tasks in a reproducible way. The pipeline is deployed on a web-based platform and the server is user-friendly, flexible, and has been designed to be scalable according to the specific requirements. This pipeline exhibits high performance for 13 benchmark datasets including both binary and multi-class classification tasks. In addition, to facilitate the application of mAML and expand the human disease-related microbiome learning repository, we developed GMrepo ML repository (GMrepo Microbiome Learning repository) from the GMrepo database. The repository involves 120 microbial classification tasks for 85 human-disease phenotypes referring to 12,429 metagenomic samples and 38,643 amplicon samples. The mAML pipeline and the GMrepo ML repository are expected to be important resources for researches in microbiology and algorithm developments.Database URLhttp://39.100.246.211:8050/Home

scholarly journals An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

2021 ◽  
Vol 11 (21) ◽  
pp. 10301
Author(s):  
Muhammad Shoaib Farooq ◽  
Attique Ur Rehman ◽  
Muhammad Idrees ◽  
Muhammad Ahsan Raza ◽  
Jehad Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real World ◽  
Early Stage ◽  
Binary Classification ◽  
X Rays ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

scholarly journals Different modes of runaway electron beams generated in high-pressure gases

2021 ◽  
Vol 2064 (1) ◽  
pp. 012001
Author(s):  
V F Tarasenko ◽  
D A Sorokin ◽  
D V Beloplotov ◽  
M I Lomaev ◽  
E Kh Baksht ◽  
...  
Keyword(s):  
High Pressure ◽  
Runaway Electron ◽  
Experimental Studies ◽  
Beam Current ◽  
Current Amplitude ◽  
X Rays ◽  
X Ray ◽  
Beam Current Amplitude ◽  
The One ◽  
Direction Opposite

Abstract This article presents the results of experimental studies of different modes of a runaway electron beam (RAEB) generation in high-pressure gases as well as X-rays caused by it. In particular, the mode with the greatest beam current amplitude, the one with two current pulses, that with the X-ray pulse duration of 100s ns, the mode in which a RAEB propagates in the direction opposite from an anode, and some others are described. The effect of the cathode design and material on the RAEB current amplitude and duration in atmospheric-pressure air is shown. When analyzing the most common modes, the features of the gap breakdown are used.

scholarly journals COVIDPEN: A Novel COVID-19 Detection Model using Chest X-Rays and CT Scans

2020 ◽  
Author(s):  
Amit Kumar Jaiswal ◽  
Prayag Tiwari ◽  
Vipin Kumar Rathi ◽  
Jia Qian ◽  
Hari Mohan Pandey ◽  
...  
Keyword(s):  
False Negative ◽  
Healthcare Providers ◽  
False Negative Rate ◽  
X Rays ◽  
X Ray ◽  
Detection Model ◽  
Global Pandemic ◽  
High False Negative Rate ◽  
Proposed Model ◽  
Chest X Ray

The trending global pandemic of COVID-19 is the fastest ever impact which caused people worldwide by severe acute respiratory syndrome~(SARS)-driven coronavirus. However, several countries suffer from the shortage of test kits and high false negative rate in PCR test. Enhancing the chest X-ray or CT detection rate becomes critical. The patient triage is of utmost importance and the use of machine learning can drive the diagnosis of chest X-ray or CT image by identifying COVID-19 cases. To tackle this problem, we propose~COVIDPEN~-~a transfer learning approach on Pruned EfficientNet-based model for the detection of COVID-19 cases. The proposed model is further interpolated by post-hoc analysis for the explainability of the predictions. The effectiveness of our proposed model is demonstrated on two systematic datasets of chest radiographs and computed tomography scans. Experimental results with several baseline comparisons show that our method is on par and confers clinically explicable instances, which are meant for healthcare providers.

scholarly journals Measurements of fast electron beams and soft X-ray emission from plasma-focus experiments

Nukleonika ◽  
2016 ◽  
Vol 61 (2) ◽  
pp. 161-167 ◽  
Author(s):  
Władysław Surała ◽  
Marek J. Sadowski ◽  
Roch Kwiatkowski ◽  
Lech Jakubowski ◽  
Jarosław Żebrowski
Keyword(s):  
Plasma Focus ◽  
Fast Electron ◽  
Hot Spots ◽  
Electron Beams ◽  
Experimental Studies ◽  
Neutron Counter ◽  
X Rays ◽  
Strong Electron ◽  
Time Resolved ◽  
X Ray

Abstract The paper reports results of the recent experimental studies of pulsed electron beams and soft X-rays in plasma-focus (PF) experiments carried out within a modified PF-360U facility at the NCBJ, Poland. Particular attention was focused on time-resolved measurements of the fast electron beams by means of two different magnetic analyzers, which could record electrons of energy ranging from about 41 keV to about 715 keV in several (6 or 8) measuring channels. For discharges performed with the pure deuterium filling, many strong electron signals were recorded in all the measuring channels. Those signals were well correlated with the first hard X-ray pulse detected by an external scintillation neutron-counter. In some of the analyzer channels, electron spikes (lasting about dozens of nanoseconds) and appearing in different instants after the current peculiarity (so-called current dip) were also recorded. For several discharges, fast ion beams, which were emitted along the z-axis and recorded with nuclear track detectors, were also investigated. Those measurements confirmed a multibeam character of the ion emission. The time-integrated soft X-ray images, which were taken side-on by means of a pinhole camera and sensitive X-ray films, showed the appearance of some filamentary structures and so-called hot spots. The application of small amounts of admixtures of different heavy noble gases, i.e. of argon (4.8% volumetric), krypton (1.6% volumetric), or xenon (0.8% volumetric), decreased intensity of the recorded electron beams, but increased intensity of the soft X-ray emission and showed more distinct and numerous hot spots. The recorded electron spikes have been explained as signals produced by quasi-mono-energetic microbeams emitted from tiny sources (probably plasma diodes), which can be formed near the observed hot spots.

scholarly journals Deep COVID 19: Deep Learning for COVID 19 Detection from X ray Images

2021 ◽  
Vol 11 (1) ◽  
pp. 1-6
Author(s):  
Ahmed Hashem El Fiky ◽  
Keyword(s):  
Machine Learning Algorithms ◽  
Diagnostic Process ◽  
X Rays ◽  
Virus Spread ◽  
Large Dataset ◽  
X Ray ◽  
Support Mechanism ◽  
Proposed Model ◽  
Speed Up ◽  
First Time

The COVID-19 will take place for the first time in December 2019 in Wuhan, China. After that, the virus spread all over the world, with over 4.7 million confirmed cases and over 315000 deaths as of the time of writing this report. Radiologists can employ machine learning algorithms developed on radiography pictures as a decision support mechanism to help them speed up the diagnostic process. The goal of this study is to conduct a quantitative evaluation of six off-the-shelf convolutional neural networks (CNNs) for COVID-19 X-ray image analysis. Due to the limited amount of images available for analysis, the CNN transfer learning approach was used. We also developed a simple CNN architecture with a modest number of parameters that does a good job of differentiating COVID-19 from regular X-rays. in this paper, we are used large dataset which contained CXR images of normal patients and patients with COVID-19. the number of CXR images for normal patients are 10,192 image and the number of CXR images for COVID-19 patients are 3,616 images. The results of experiments show the effectiveness and robustness of Deep-COVID-19 and pretrained models like VGG16, VGG19, and MobileNets. Our proposed Model Deep-COVID-19 achieved over 94.5% accuracy.

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