scholarly journals Dangerous goods identification based on multi-channel neural network

2021 ◽  
Vol 2082 (1) ◽  
pp. 012008
Author(s):  
XiaoTian Wei ◽  
ZiQiang Hao ◽  
Bo Du
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Artificial Eye ◽  
Data Set ◽  
X Ray ◽  
Dangerous Goods ◽  
Public Data ◽  
Increasing Demand ◽  
Security Inspection ◽  
Eye Recognition

Abstract In the current society, there is an increasing demand for dangerous goods identification technology in X-ray images, but at the current stage, most of the identification of dangerous goods in X-ray images still relies on artificial eye recognition. In order to solve this problem, this paper proposes A method for automatically and intelligently identifying dangerous goods in X-ray images based on the transformation of the convolutional neural network. By adding multi-channel convolution and normalization to the convolutional neural network, the target features are extracted to achieve automatic detection of dangerous goods. The purpose of better identification. In the simulation experiment, using the public data set and self-built data set in the X-ray security inspection field, the accuracy of the identification of dangerous goods in the X-ray image was obtained more satisfactory results than the traditional dangerous goods identification. The improved Alex Net network’ s testing accuracy on contraband knives and guns is 8.53% and 11.6% higher than the training accuracy of the original Alex Net network.

scholarly journals On Using XMC R-CNN Model for Contraband Detection within X-Ray Baggage Security Images

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yong Zhang ◽  
Weiwu Kong ◽  
Dong Li ◽  
Xudong Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection Rate ◽  
Automatic Detection ◽  
User Needs ◽  
Labor Intensity ◽  
Detection Rates ◽  
X Ray ◽  
Detection Technology ◽  
Security Inspection

We present an X-ray material classifier region-based convolutional neural network (XMC R-CNN) model for detecting the typical guns and the typical knives in X-ray baggage images. The XMC R-CNN model is used to solve the problem of contraband detection in overlapped X-ray baggage images by the X-ray material classifier algorithm and the organic stripping and inorganic stripping algorithm, and better detection rate and the miss rate are achieved. The detection rates of guns and knives are 96.5% and 95.8%, and the miss rates of guns and knives are 2.2% and 4.2%. The contraband detection technology based on the XMC R-CNN model is applied to X-ray baggage images of security inspection. According to user needs, the safe X-ray baggage images can be automatically filtered in some specific fields, which reduces the number of X-ray baggage images that security inspectors need to screen. The efficiency of security inspection is improved, and the labor intensity of security inspection is reduced. In addition, the security inspector can screen X-ray baggage images according to the boxes of automatic detection, which can improve the effect of security inspection.

scholarly journals Estimating Gender and Age from Brain Structural MRI of Children and Adolescents: A 3D Convolutional Neural Network Multitask Learning Model

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Sergio Leonardo Mendes ◽  
Walter Hugo Lopez Pinaya ◽  
Pedro Pan ◽  
João Ricardo Sato
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Learning Model ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Multitask Learning ◽  
Data Set ◽  
Graphical Output ◽  
Gender And Age ◽  
Public Data

Despite recent advances, assessing biological measurements for neuropsychiatric disorders is still a challenge, where confounding variables such as gender and age (as a proxy for neurodevelopment) play an important role. This study explores brain structural magnetic resonance imaging (sMRI) from two public data sets (ABIDE-II and ADHD-200) with healthy control (HC, N = 894), autism spectrum disorder (ASD, N = 251), and attention deficit hyperactivity disorder (ADHD, N = 357) individuals. We used gray and white matter preprocessed via voxel-based morphometry (VBM) to train a 3D convolutional neural network with a multitask learning strategy to estimate gender, age, and mental health status from structural brain differences. Gradient-based methods were employed to generate attention maps, providing clinically relevant identification of most representative brain regions for models’ decision-making. This approach resulted in satisfactory predictions for gender and age. ADHD-200-trained models, evaluated in 10-fold cross-validation procedures on test set, obtained a mean absolute error (MAE) of 1.43 years (±0.22 SD) for age prediction and an area under the curve (AUC) of 0.85 (±0.04 SD) for gender classification. In out-of-sample validation, the best-performing ADHD-200 models satisfactorily predicted age (MAE = 1.57 years) and gender (AUC = 0.89) in the ABIDE-II data set. The models’ accuracy was in line with the current state-of-the-art machine learning applications in neuroimaging. Key regions for models’ accuracy were presented as a meaningful graphical output. New implementations, such as the use of VBM along with a 3D convolutional neural network multitask learning model and a brain imaging graphical output, reinforce the relevance of the proposed workflow.

scholarly journals Implementation of convolutional neural network approach for COVID-19 disease detection

2020 ◽  
Vol 52 (12) ◽  
pp. 590-601
Author(s):  
Emrah Irmak
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Clinical Data ◽  
Data Sets ◽  
Grid Search ◽  
Neural Network Approach ◽  
Data Set ◽  
X Ray ◽  
Average Accuracy ◽  
Chest X Ray

In this paper, two novel, powerful, and robust convolutional neural network (CNN) architectures are designed and proposed for two different classification tasks using publicly available data sets. The first architecture is able to decide whether a given chest X-ray image of a patient contains COVID-19 or not with 98.92% average accuracy. The second CNN architecture is able to divide a given chest X-ray image of a patient into three classes (COVID-19 versus normal versus pneumonia) with 98.27% average accuracy. The hyperparameters of both CNN models are automatically determined using Grid Search. Experimental results on large clinical data sets show the effectiveness of the proposed architectures and demonstrate that the proposed algorithms can overcome the disadvantages mentioned above. Moreover, the proposed CNN models are fully automatic in terms of not requiring the extraction of diseased tissue, which is a great improvement of available automatic methods in the literature. To the best of the author’s knowledge, this study is the first study to detect COVID-19 disease from given chest X-ray images, using CNN, whose hyperparameters are automatically determined by the Grid Search. Another important contribution of this study is that it is the first CNN-based COVID-19 chest X-ray image classification study that uses the largest possible clinical data set. A total of 1,524 COVID-19, 1,527 pneumonia, and 1524 normal X-ray images are collected. It is aimed to collect the largest number of COVID-19 X-ray images that exist in the literature until the writing of this research paper.

scholarly journals The diagnostic evaluation of Convolutional Neural Network (CNN) for the assessment of chest X-ray of patients infected with COVID-19

2020 ◽  
Author(s):  
Syed Usama Khalid Bukhari ◽  
Syed Safwan Khalid Bukhari ◽  
Asmara Syed ◽  
Syed Sajid Hussain Shah
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Health Professionals ◽  
Digital Images ◽  
Microbial Pathogens ◽  
X Rays ◽  
Data Set ◽  
X Ray ◽  
Radiological Abnormality ◽  
Chest X Ray

AbstractIntroductionThe main target of COVID-19 is the lungs where it may cause pneumonia in severely ill patients. Chest X-ray is an important diagnostic test to assess the lung for the damaging effects of COVID-19. Many other microbial pathogens can also cause damage to lungs leading to pneumonia but there are certain radiological features which can favor the diagnosis of pneumonia caused by COVID-19. With the rising number of cases of COVID-19, it would be imperative to develop computer programs which may assist the health professionals in the prevailing scenario.Materials & MethodsA total of two hundred and seventy eight (278) images of chest X-rays have been assessed by applying ResNet-50 convolutional neural network architectures in the present study. The digital images were acquired from the public repositories provided by University of Montreal and National Institutes of Health. These digital images of Chest X-rays were divided into three groups labeled as normal, pneumonia and COVID-19. The third group contains digital images of chest X-rays of patients diagnosed with COVID-19 infection while the second group contains images of lung with pneumonia caused by other pathogens.ResultsThe radiological images included in the data set are 89 images of lungs with COVID-19 infection, 93 images of lungs without any radiological abnormality and 96 images of patient with pneumonia caused by other pathogens. In this data set, 80% of the images were employed for training, and 20% for testing. A pre-trained (on ImageNet data set) ResNet-50 architecture was used to diagnose the cases of COVID-19 infections on lung X-ray images. The analysis of the data revealed that computer vision based program achieved diagnostic accuracy of 98.18 %, and F1-score of 98.19.ConclusionThe performance of convolutional neural network regarding the differentiation of pulmonary changes caused by COVID-19 from the other type of pneumonias on digital images of the chest X-rays is excellent and it may be an extremely useful adjunct tool for the health professionals.

scholarly journals End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

2021 ◽  
Vol 7 (2) ◽  
pp. 356-362
Author(s):  
Harry Coppock ◽  
Alex Gaskell ◽  
Panagiotis Tzirakis ◽  
Alice Baird ◽  
Lyn Jones ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Low Cost ◽  
Area Under The Curve ◽  
Alternative Form ◽  
Economic Damage ◽  
Audio Signals ◽  
Operating Characteristics ◽  
Data Set ◽  
Empirical Performance

BackgroundSince the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution.MethodsThis study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings.ResultsOur model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification.ConclusionThis study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.

Sign in / Sign up

Export Citation Format

Share Document