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

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.

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Dangerous goods identification based on multi-channel neural network

Journal of Physics Conference Series ◽

10.1088/1742-6596/2082/1/012008 ◽

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.

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Automatic Detection of Tuberculosis from Chest X-Rays using Convolutional Neural Network

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e9292.069520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 72-77

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Early Stage ◽

Automatic Detection ◽

X Rays ◽

X Ray ◽

Chest X Ray ◽

Causes Of Deaths ◽

Graph Cut Segmentation ◽

Better Than

Tuberculosis is one of the single infectious diseases which is one among the top ten causes of deaths. Eradication is only possible by timely diagnosis of disease and treatment at its early stage. But unfortunately, timely detection is lagging due to many reasons. In this angle we present a novel scheme for automatic detection of tuberculosis from chest X-ray images. The proposed method accurately detects the malady by performing graph cut segmentation followed by classification using convolutional neural network. The classifier facilitates the chest X-rays to be classified as normal or abnormal. Simulation results show that the accuracy of 94%, sensitivity of 96% and specificity of 84% obtained from the proposed system are comparable and even better than the existing reported methods.

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Constructing the First Convolutional Neural Network for Determining Damaged Bones and Normal Bones in X-Ray Images

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i11.5255 ◽

2019 ◽

Vol 7 (11) ◽

pp. 52-55

Author(s):

Akshansh Mishra ◽

Priyankan Datta

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

X Ray

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Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191122114608 ◽

2019 ◽

Vol 13 ◽

Author(s):

Niha Kamal Basha ◽

Aisha Banu Wahab

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Monitoring System ◽

Single Channel ◽

Confusion Matrix ◽

Sudden Change ◽

Automatic Detection ◽

Support Vector ◽

Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

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