scholarly journals Automatic detection of COVID-19 in chest radiographs using serially concatenated deep and handcrafted features

2021 ◽  
pp. 1-14
Author(s):  
S. Rajesh Kannan ◽  
J. Sivakumar ◽  
P. Ezhilarasi
Keyword(s):  
Vital Role ◽  
Detection Methods ◽  
Occurrence Rate ◽  
Detection Accuracy ◽  
X Ray ◽  
Contagious Diseases ◽  
Disease Occurrence ◽  
Chest X Ray ◽  
Feature Based ◽  
Serially Concatenated

Since the infectious disease occurrence rate in the human community is gradually rising due to varied reasons, appropriate diagnosis and treatments are essential to control its spread. The recently discovered COVID-19 is one of the contagious diseases, which infected numerous people globally. This contagious disease is arrested by several diagnoses and handling actions. Medical image-supported diagnosis of COVID-19 infection is an approved clinical practice. This research aims to develop a new Deep Learning Method (DLM) to detect the COVID-19 infection using the chest X-ray. The proposed work implemented two methods namely, detection of COVID-19 infection using (i) a Firefly Algorithm (FA) optimized deep-features and (ii) the combined deep and machine features optimized with FA. In this work, a 5-fold cross-validation method is engaged to train and test detection methods. The performance of this system is analyzed individually resulting in the confirmation that the deep feature-based technique helps to achieve a detection accuracy of >  92% with SVM-RBF classifier and combining deep and machine features achieves >  96% accuracy with Fine KNN classifier. In the future, this technique may have potential to play a vital role in testing and validating the X-ray images collected from patients suffering from the infection diseases.

scholarly journals COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

2020 ◽  
Author(s):  
Tahmina Zebin ◽  
Shahadate Rezvy ◽  
Wei Pang
Keyword(s):  
Disease Progression ◽  
Detection Accuracy ◽  
X Rays ◽  
Automated Classification ◽  
X Ray ◽  
Feature Extractor ◽  
Chest X Ray ◽  
Activation Mapping ◽  
Activation Map

Abstract Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We applied and implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets {https://github.com/ieee8023/covid-chestxray-dataset},{https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia}}. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and pneumonia (viral and bacterial) from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 91.2% , 95.3%, 96.7% for the VGG16, ResNet50 and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a cycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we visualized the regions of input that are important for predictions and a gradient class activation mapping (Grad-CAM) technique is used in the pipeline to produce a coarse localization map of the highlighted regions in the image. This activation map can be used to monitor affected lung regions during disease progression and severity stages.

scholarly journals A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images

2021 ◽  
Vol 18 (22) ◽  
pp. 12191
Author(s):  
Prabhjot Kaur ◽  
Shilpi Harnal ◽  
Rajeev Tiwari ◽  
Fahd S. Alharithi ◽  
Ahmed H. Almulihi ◽  
...  
Keyword(s):  
Health Care Systems ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Short Supply ◽  
X Ray ◽  
Proposed Model ◽  
Long Time ◽  
Care Systems ◽  
Chest X Ray ◽  
Multiclass Svm

COVID-19 declared as a pandemic that has a faster rate of infection and has impacted the lives and the country’s economy due to forced lockdowns. Its detection using RT-PCR is required long time and due to which its infection has grown exponentially. This creates havoc for the shortage of testing kits in many countries. This work has proposed a new image processing-based technique for the health care systems named “C19D-Net”, to detect “COVID-19” infection from “Chest X-Ray” (XR) images, which can help radiologists to improve their accuracy of detection COVID-19. The proposed system extracts deep learning (DL) features by applying the InceptionV4 architecture and Multiclass SVM classifier to classify and detect COVID-19 infection into four different classes. The dataset of 1900 Chest XR images has been collected from two publicly accessible databases. Images are pre-processed with proper scaling and regular feeding to the proposed model for accuracy attainments. Extensive tests are conducted with the proposed model (“C19D-Net”) and it has succeeded to achieve the highest COVID-19 detection accuracy as 96.24% for 4-classes, 95.51% for three-classes, and 98.1% for two-classes. The proposed method has outperformed well in expressions of “precision”, “accuracy”, “F1-score” and “recall” in comparison with most of the recent previously published methods. As a result, for the present situation of COVID-19, the proposed “C19D-Net” can be employed in places where test kits are in short supply, to help the radiologists to improve their accuracy of detection of COVID-19 patients through XR-Images.

scholarly journals Defect Detection of Industry Wood Veneer Based on NAS and Multi-Channel Mask R-CNN

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4398 ◽  
Author(s):  
Jiahao Shi ◽  
Zhenye Li ◽  
Tingting Zhu ◽  
Dongyi Wang ◽  
Chao Ni
Keyword(s):  
Neural Network ◽  
Defect Detection ◽  
Industrial Applications ◽  
Vital Role ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Wood Veneer ◽  
Multiple Channel ◽  
Execution Speed ◽  
Using Data

Wood veneer defect detection plays a vital role in the wood veneer production industry. Studies on wood veneer defect detection usually focused on detection accuracy for industrial applications but ignored algorithm execution speed; thus, their methods do not meet the required speed of online detection. In this paper, a new detection method is proposed that achieves high accuracy and a suitable speed for online production. Firstly, 2838 wood veneer images were collected using data collection equipment developed in the laboratory and labeled by experienced workers from a wood company. Then, an integrated model, glance multiple channel mask region convolution neural network (R-CNN), was constructed to detect wood veneer defects, which included a glance network and a multiple channel mask R-CNN. Neural network architect search technology was used to automatically construct the glance network with the lowest number of floating-point operations to pick out potential defect images out of numerous original wood veneer images. A genetic algorithm was used to merge the intermediate features extracted by the glance network. Multi-Channel Mask R-CNN was then used to classify and locate the defects. The experimental results show that the proposed method achieves a 98.70% overall classification accuracy and a 95.31% mean average precision, and only 2.5 s was needed to detect a batch of 50 standard images and 50 defective images. Compared with other wood veneer defect detection methods, the proposed method is more accurate and faster.

scholarly journals COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

2020 ◽  
Author(s):  
Tahmina Zebin ◽  
Shahadate Rezvy
Keyword(s):  
Disease Progression ◽  
Input Image ◽  
Mapping Technique ◽  
Detection Accuracy ◽  
X Rays ◽  
Automated Classification ◽  
X Ray ◽  
Chest X Ray ◽  
Activation Mapping

Abstract Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets1,2. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and Pneumonia from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 90%, 94.3%, and 96.8% for the VGG16, ResNet50, and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a CycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we implemented a gradient class activation mapping technique to highlight the regions of the input image that are important for predictions. Additionally, these visualizations can be used to monitor the affected lung regions during disease progression and severity stages.

scholarly journals An Artificial Intelligence–Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study

2021 ◽  
Vol 4 (12) ◽  
pp. e2141096
Author(s):  
Fatemeh Homayounieh ◽  
Subba Digumarthy ◽  
Shadi Ebrahimian ◽  
Johannes Rueckel ◽  
Boj Friedrich Hoppe ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Multicenter Study ◽  
Detection Accuracy ◽  
X Ray ◽  
Nodule Detection ◽  
Chest X Ray

scholarly journals CHEST X-RAY FINDINGS IN COVID-19 PNEUMONIA: A PICTORIAL REVIEW

2020 ◽  
pp. 9-11
Author(s):  
Zohra Ahmad ◽  
Parul Dutta ◽  
Deepjyoti Das Choudhury ◽  
Satabdi Kalita ◽  
Zohaib Hussain ◽  
...  
Keyword(s):  
Virus Disease ◽  
Vital Role ◽  
World Health ◽  
Thoracic Imaging ◽  
X Ray ◽  
Pictorial Review ◽  
Care Workers ◽  
The World ◽  
Chest X Ray ◽  
Health Organization

Corona Virus Disease 19 or COVID-19, was first detected in Wuhan province in China in December 2019 and reported to the World Health Organization (WHO) on December 31, 2019 [1]. It was declared a pandemic on March 11th, 2020 [2] and has till now affected 40 million people all around the world resulting in 1.1 million deaths (as of 18th Oct, 2020) [3]. As the world is reeling under the burden of the disease, it has been imperative for the radiologists to be familiar with the imaging appearance of the disease. Thoracic imaging with chest X-ray and CT is the key modality for the diagnosis and management of respiratory diseases. Although CT is more sensitive, the immense challenge of disinfection control in the modality may disrupt the service availability and portable X-ray may be considered to minimize the risk [4]. Use of portable X-ray has played a vital role in all the areas around the world during this pandemic. The purpose of this pictorial review is to represent the frequently encountered features and abnormalities in chest X-ray and strengthen the knowledge of the health-care workers in this war against the pandemic.

scholarly journals Detection of COVID-19 from Chest X-ray Images Using Deep Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5940
Author(s):  
Natheer Khasawneh ◽  
Mohammad Fraiwan ◽  
Luay Fraiwan ◽  
Basheer Khassawneh ◽  
Ali Ibnian
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Detection Accuracy ◽  
Deep Convolutional Neural Networks ◽  
Local Data ◽  
X Ray ◽  
The Public ◽  
Global Pandemic ◽  
Chest X Ray ◽  
Public Dataset

The COVID-19 global pandemic has wreaked havoc on every aspect of our lives. More specifically, healthcare systems were greatly stretched to their limits and beyond. Advances in artificial intelligence have enabled the implementation of sophisticated applications that can meet clinical accuracy requirements. In this study, customized and pre-trained deep learning models based on convolutional neural networks were used to detect pneumonia caused by COVID-19 respiratory complications. Chest X-ray images from 368 confirmed COVID-19 patients were collected locally. In addition, data from three publicly available datasets were used. The performance was evaluated in four ways. First, the public dataset was used for training and testing. Second, data from the local and public sources were combined and used to train and test the models. Third, the public dataset was used to train the model and the local data were used for testing only. This approach adds greater credibility to the detection models and tests their ability to generalize to new data without overfitting the model to specific samples. Fourth, the combined data were used for training and the local dataset was used for testing. The results show a high detection accuracy of 98.7% with the combined dataset, and most models handled new data with an insignificant drop in accuracy.

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