scholarly journals COVID-19 diagnosis from chest x-rays: developing a simple, fast, and accurate neural network

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Vasilis Nikolaou ◽  
Sebastiano Massaro ◽  
Masoud Fakhimi ◽  
Lampros Stergioulas ◽  
Wolfgang Garn
Keyword(s):  
Predictive Value ◽  
Data Augmentation ◽  
Early Stage ◽  
High Accuracy ◽  
Viral Pneumonia ◽  
Fine Tuning ◽  
Dense Layer ◽  
X Rays ◽  
X Ray ◽  
Normal Lungs

Abstract Purpose Chest x-rays are a fast and inexpensive test that may potentially diagnose COVID-19, the disease caused by the novel coronavirus. However, chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonias. Recent research using deep learning may help overcome this issue as convolutional neural networks (CNNs) have demonstrated high accuracy of COVID-19 diagnosis at an early stage. Methods We used the COVID-19 Radiography database [36], which contains x-ray images of COVID-19, other viral pneumonia, and normal lungs. We developed a CNN in which we added a dense layer on top of a pre-trained baseline CNN (EfficientNetB0), and we trained, validated, and tested the model on 15,153 X-ray images. We used data augmentation to avoid overfitting and address class imbalance; we used fine-tuning to improve the model’s performance. From the external test dataset, we calculated the model’s accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Results Our model differentiated COVID-19 from normal lungs with 95% accuracy, 90% sensitivity, and 97% specificity; it differentiated COVID-19 from other viral pneumonia and normal lungs with 93% accuracy, 94% sensitivity, and 95% specificity. Conclusions Our parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19.

scholarly journals Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8219
Author(s):  
Amin Ul Haq ◽  
Jian Ping Li ◽  
Sultan Ahmad ◽  
Shakir Khan ◽  
Mohammed Ali Alshara ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Classification Accuracy ◽  
High Performance ◽  
Data Augmentation ◽  
Early Stage ◽  
Accurate Diagnosis ◽  
Fine Tuning ◽  
Data Set ◽  
X Ray ◽  
Chest X Ray

COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread. The automatic diagnosis system is significantly necessary for COVID-19 detection. To diagnose COVID-19 from chest X-ray images, employing artificial intelligence techniques based methods are more effective and could correctly diagnosis it. The existing diagnosis methods of COVID-19 have the problem of lack of accuracy to diagnosis. To handle this problem we have proposed an efficient and accurate diagnosis model for COVID-19. In the proposed method, a two-dimensional Convolutional Neural Network (2DCNN) is designed for COVID-19 recognition employing chest X-ray images. Transfer learning (TL) pre-trained ResNet-50 model weight is transferred to the 2DCNN model to enhanced the training process of the 2DCNN model and fine-tuning with chest X-ray images data for final multi-classification to diagnose COVID-19. In addition, the data augmentation technique transformation (rotation) is used to increase the data set size for effective training of the R2DCNNMC model. The experimental results demonstrated that the proposed (R2DCNNMC) model obtained high accuracy and obtained 98.12% classification accuracy on CRD data set, and 99.45% classification accuracy on CXI data set as compared to baseline methods. This approach has a high performance and could be used for COVID-19 diagnosis in E-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 Deep Learning of COVID-19 Chest X-Rays: New Models or Fine Tuning?

2020 ◽  
Author(s):  
Tuan Pham
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Data Augmentation ◽  
Dominant Role ◽  
Care Center ◽  
Characteristic Curve ◽  
Fine Tuning ◽  
Urgent Care ◽  
X Rays ◽  
Training Time

Chest X-rays have been found to be very promising for assessing COVID-19 patients, especially for resolving emergency-department and urgent-care-center overcapacity. Deep-learning (DL) methods in artificial intelligence (AI) play a dominant role as high-performance classifiers in the detection of the disease using chest X-rays. While many new DL models have been being developed for this purpose, this study aimed to investigate the fine tuning of pretrained convolutional neural networks (CNNs) for the classification of COVID-19 using chest X-rays. Three pretrained CNNs, which are AlexNet, GoogleNet, and SqueezeNet, were selected and fine-tuned without data augmentation to carry out 2-class and 3-class classification tasks using 3 public chest X-ray databases. In comparison with other recently developed DL models, the 3 pretrained CNNs achieved very high classification results in terms of accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver-operating-characteristic curve. AlexNet, GoogleNet, and SqueezeNet require the least training time among pretrained DL models, but with suitable selection of training parameters, excellent classification results can be achieved without data augmentation by these networks. The findings contribute to the urgent need for harnessing the pandemic by facilitating the deployment of AI tools that are fully automated and readily available in the public domain for rapid implementation.

Ultrasound Detection of Pneumothorax Compared with Chest X-Ray and Computed Tomography Scan

2011 ◽  
Vol 77 (4) ◽  
pp. 480-483 ◽  
Author(s):  
Khanjan Nagarsheth ◽  
Stanley Kurek
Keyword(s):  
Ct Scan ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Chest Ct ◽  
Trauma Patients ◽  
X Rays ◽  
Thoracic Ultrasound ◽  
X Ray ◽  
Chest X Ray ◽  
Chest Ct Scan

Pneumothorax after trauma can be a life threatening injury and its care requires expeditious and accurate diagnosis and possible intervention. We performed a prospective, single blinded study with convenience sampling at a Level I trauma center comparing thoracic ultrasound with chest X-ray and CT scan in the detection of traumatic pneumothorax. Trauma patients that received a thoracic ultrasound, chest X-ray, and chest CT scan were included in the study. The chest X-rays were read by a radiologist who was blinded to the thoracic ultrasound results. Then both were compared with CT scan results. One hundred and twenty-five patients had a thoracic ultrasound performed in the 24-month period. Forty-six patients were excluded from the study due to lack of either a chest X-ray or chest CT scan. Of the remaining 79 patients there were 22 positive pneumothorax found by CT and of those 18 (82%) were found on ultrasound and 7 (32%) were found on chest X-ray. The sensitivity of thoracic ultrasound was found to be 81.8 per cent and the specificity was found to be 100 per cent. The sensitivity of chest X-ray was found to be 31.8 per cent and again the specificity was found to be 100 per cent. The negative predictive value of thoracic ultrasound for pneumothorax was 0.934 and the negative predictive value for chest X-ray for pneumothorax was found to be 0.792. We advocate the use of chest ultrasound for detection of pneumothorax in trauma patients.

scholarly journals QUALITY CONTROL X RAYS MAMMOGRAPHY IN THE EFFORT MITIGATION INCREASING NUMBER OF CANCER PATIENTS

2017 ◽  
Vol 17 (1) ◽  
pp. 45
Author(s):  
Rini Safitri ◽  
Evi Yufita
Keyword(s):  
Breast Cancer ◽  
Quality Control ◽  
Early Stage ◽  
Input Voltage ◽  
Test Subject ◽  
X Rays ◽  
Radiographic Image ◽  
X Ray ◽  
Radiation Output

Abstract. Early detection of breast cancer is the first step in prevention that can be done by women, therefore when one is diagnosed with breast cancer, the appropriate treatment can be performed quickly and accurately. Early diagnosis of breast cancer can be a way of mitigation in preventing breast cancer through the use of mammography. Bureau of Radiology as said by The Joint Commission on Accreditation of Hospitals (JHCA) stated that one of the responsibilities of the radiology unit is to control the quality of service which aims to minimize the radiographic image repetition factor; as well as maximizes the quality of radiographic image. Quality control tests are an effort that is needed on the mammography X-ray diagnostics tools. This is done to maintain the quality of expected output. The parameters that are included within the radiation output are the magnitude of current and the voltage of tube that are produced; therefore they remained constant and correspond to the recommended standard. Bureau of Radiological Health, as said by JHCA mentioned that to control the quality of image which will minimize the radiographic image repetition and maximizes the quality of radiographic image. Therefore the radiation output will not be dangerous later. The early stage of the quality control test on the machine was conducted by setting all the filtrations which were placed to capture the x-ray on the x-ray plane tube with minimum value. Then, ionization chamber is placed on the test subject points; right after that the distance between it to the radiation source is noted. The x-ray film is place on a film on the compression table of the patient and the distance between film and the focus point is noted. This is then exposed using a target filter Mo/Mo by setting the current as well as variation the voltage and time. The standard voltage measurements are 20-33kVp. This data is from the observations of time exposure; the output value is then noted. The above procedure is conducted from the minimum voltage to the maximum voltage. The output ray is measured for each voltage. The same procedure is conducted to the target filter Mo/RH. The results obtained are that the greater the input voltage and current will subsequently produce greater doses, therefore the exposure has exceeded the standard limit 0.1 mHy with longer exposure time. The HVL density thickness on the mammography X-ray machine determined the quality of the beam and the doses of x-ray exposure on the mammography machine. The output stability of x-ray beam exposure in the mammography machine mode Mo/Mo still fulfill the standard which is the value of 69% Keywords: Quality Control, Sinar-X, mammography, Mp/Mo, Mo/RH

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 An Improved VGG16 Model for Pneumonia Image Classification

2021 ◽  
Vol 11 (23) ◽  
pp. 11185
Author(s):  
Zhi-Peng Jiang ◽  
Yi-Yang Liu ◽  
Zhen-En Shao ◽  
Ko-Wei Huang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Medical Image ◽  
Best Practice ◽  
Data Augmentation ◽  
X Rays ◽  
Neural Network Models ◽  
X Ray ◽  
Medical Image Recognition

Image recognition has been applied to many fields, but it is relatively rarely applied to medical images. Recent significant deep learning progress for image recognition has raised strong research interest in medical image recognition. First of all, we found the prediction result using the VGG16 model on failed pneumonia X-ray images. Thus, this paper proposes IVGG13 (Improved Visual Geometry Group-13), a modified VGG16 model for classification pneumonia X-rays images. Open-source thoracic X-ray images acquired from the Kaggle platform were employed for pneumonia recognition, but only a few data were obtained, and datasets were unbalanced after classification, either of which can result in extremely poor recognition from trained neural network models. Therefore, we applied augmentation pre-processing to compensate for low data volume and poorly balanced datasets. The original datasets without data augmentation were trained using the proposed and some well-known convolutional neural networks, such as LeNet AlexNet, GoogLeNet and VGG16. In the experimental results, the recognition rates and other evaluation criteria, such as precision, recall and f-measure, were evaluated for each model. This process was repeated for augmented and balanced datasets, with greatly improved metrics such as precision, recall and F1-measure. The proposed IVGG13 model produced superior outcomes with the F1-measure compared with the current best practice convolutional neural networks for medical image recognition, confirming data augmentation effectively improved model accuracy.

scholarly journals Training deep learning algorithms with weakly labeled pneumonia chest X-ray data for COVID-19 detection

2020 ◽  
Author(s):  
Sivaramakrishnan Rajaraman ◽  
Sameer Antani
Keyword(s):  
Data Augmentation ◽  
Feature Space ◽  
Distinct Pattern ◽  
Viral Pneumonia ◽  
Training Data ◽  
Polymerase Chain Reaction Test ◽  
X Ray ◽  
Chest X Ray ◽  
Baseline Test ◽  
Chain Reaction Test

AbstractThe novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused a pandemic resulting in over 2.7 million infected individuals and over 190,000 deaths and growing. Respiratory disorders in COVID-19 caused by the virus commonly present as viral pneumonia-like opacities in chest X-ray images which are used as an adjunct to the reverse transcription-polymerase chain reaction test for confirmation and evaluating disease progression. The surge places high demand on medical services including radiology expertise. However, there is a dearth of sufficient training data for developing image-based automated decision support tools to alleviate radiological burden. We address this insufficiency by expanding training data distribution through use of weakly-labeled images pooled from publicly available CXR collections showing pneumonia-related opacities. We use the images in a stage-wise, strategic approach and train convolutional neural network-based algorithms to detect COVID-19 infections in CXRs. It is observed that weakly-labeled data augmentation improves performance with the baseline test data compared to non-augmented training by expanding the learned feature space to encompass variability in the unseen test distribution to enhance inter-class discrimination, reduce intra-class similarity and generalization error. Augmentation with COVID-19 CXRs from individual collections significantly improves performance compared to baseline non-augmented training and weakly-labeled augmentation toward detecting COVID-19 like viral pneumonia in the publicly available COVID-19 CXR collections. This underscores the fact that COVID-19 CXRs have a distinct pattern and hence distribution, unlike non-COVID-19 viral pneumonia and other infectious agents.

scholarly journals Data Augmentation using Auxiliary Classifier for Improved Detection of Covid 19

2021 ◽  
Vol 10 (3) ◽  
pp. 209-214
Author(s):  
Lakshmisetty Ruthvik Raj ◽  
◽  
Bitra Harsha Vardhan ◽  
Mullapudi Raghu Vamsi ◽  
Keerthikeshwar Reddy Mamilla ◽  
...  
Keyword(s):  
Data Augmentation ◽  
X Rays ◽  
Convolutional Network ◽  
X Ray ◽  
Learning Network ◽  
X Ray Imaging ◽  
Abnormal Chest ◽  
Short Period ◽  
Chest X Ray ◽  
Deep Learning Network

COVID-19 is a severe and potentially fatal respiratory infection called coronavirus 2 disease (SARS-Co-2). COVID-19 is easily detectable on an abnormal chest x-ray. Numerous extensive studies have been conducted due to the findings, demonstrating how precise the detection of coronas using X-rays within the chest is. To train a deep learning network, such as a convolutional neural network, a large amount of data is required. Due to the recent end of the pandemic, it is difficult to collect many Covid x-ray images in a short period. The purpose of this study is to demonstrate how X-ray imaging (CXR) is created using the Covid CNN model-based convolutional network. Additionally, we demonstrate that the performance of CNNs and various COVID-19 acquisition algorithms can be used to generate synthetic images from data extensions. Alone, with CNN distribution, an accuracy of 85 percent was achieved. The accuracy has been increased to 95% by adding artificial images generated from data. We anticipate that this approach will expedite the discovery of COVID-19 and result in radiological solid programs. We leverage transfer learning in this paper to reduce time complexity and achieve the highest accuracy.

scholarly journals High-Precision X-ray Total Scattering Measurements Using a High-Accuracy Detector System

2021 ◽  
Vol 7 (1) ◽  
pp. 2
Author(s):  
Kenichi Kato ◽  
Kazuya Shigeta
Keyword(s):  
High Precision ◽  
Dynamic Range ◽  
Atomic Displacement ◽  
High Accuracy ◽  
Detector System ◽  
Scattering Method ◽  
X Rays ◽  
Total Scattering ◽  
X Ray ◽  
Scattering Measurements

The total scattering method, which is based on measurements of both Bragg and diffuse scattering on an equal basis, has been still challenging even by means of synchrotron X-rays. This is because such measurements require a wide coverage in scattering vector Q, high Q resolution, and a wide dynamic range for X-ray detectors. There is a trade-off relationship between the coverage and resolution in Q, whereas the dynamic range is defined by differences in X-ray response between detector channels (X-ray response non-uniformity: XRNU). XRNU is one of the systematic errors for individual channels, while it appears to be a random error for different channels. In the present study, taking advantage of the randomness, the true sensitivity for each channel has been statistically estimated. Results indicate that the dynamic range of microstrip modules (MYTHEN, Dectris, Baden-Daettwil, Switzerland), which have been assembled for a total scattering measurement system (OHGI), has been successfully restored from 104 to 106. Furthermore, the correction algorithm has been optimized to increase time efficiencies. As a result, the correcting time has been reduced from half a day to half an hour, which enables on-demand correction for XRNU according to experimental settings. High-precision X-ray total scattering measurements, which has been achieved by a high-accuracy detector system, have demonstrated valence density studies from powder and PDF studies for atomic displacement parameters.

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