Prohibited Item Detection in Airport X-Ray Security Images via Attention Mechanism Based CNN

2018 ◽  
pp. 429-439 ◽  
Author(s):  
Maoshu Xu ◽  
Haigang Zhang ◽  
Jinfeng Yang
Keyword(s):  
Attention Mechanism ◽  
X Ray

scholarly journals Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 765
Author(s):  
Mohd Asyraf Zulkifley ◽  
Nur Ayuni Mohamed ◽  
Siti Raihanah Abdani ◽  
Nor Azwan Mohamed Kamari ◽  
Asraf Mohamed Moubark ◽  
...  
Keyword(s):  
Bone Growth ◽  
Bone Age ◽  
Attention Mechanism ◽  
Assessment System ◽  
Dominant Hand ◽  
Age Assessment ◽  
Image Normalization ◽  
X Ray ◽  
Bone Age Assessment ◽  
Skeletal Bone

Skeletal bone age assessment using X-ray images is a standard clinical procedure to detect any anomaly in bone growth among kids and babies. The assessed bone age indicates the actual level of growth, whereby a large discrepancy between the assessed and chronological age might point to a growth disorder. Hence, skeletal bone age assessment is used to screen the possibility of growth abnormalities, genetic problems, and endocrine disorders. Usually, the manual screening is assessed through X-ray images of the non-dominant hand using the Greulich–Pyle (GP) or Tanner–Whitehouse (TW) approach. The GP uses a standard hand atlas, which will be the reference point to predict the bone age of a patient, while the TW uses a scoring mechanism to assess the bone age using several regions of interest information. However, both approaches are heavily dependent on individual domain knowledge and expertise, which is prone to high bias in inter and intra-observer results. Hence, an automated bone age assessment system, which is referred to as Attention-Xception Network (AXNet) is proposed to automatically predict the bone age accurately. The proposed AXNet consists of two parts, which are image normalization and bone age regression modules. The image normalization module will transform each X-ray image into a standardized form so that the regressor network can be trained using better input images. This module will first extract the hand region from the background, which is then rotated to an upright position using the angle calculated from the four key-points of interest. Then, the masked and rotated hand image will be aligned such that it will be positioned in the middle of the image. Both of the masked and rotated images will be obtained through existing state-of-the-art deep learning methods. The last module will then predict the bone age through the Attention-Xception network that incorporates multiple layers of spatial-attention mechanism to emphasize the important features for more accurate bone age prediction. From the experimental results, the proposed AXNet achieves the lowest mean absolute error and mean squared error of 7.699 months and 108.869 months2, respectively. Therefore, the proposed AXNet has demonstrated its potential for practical clinical use with an error of less than one year to assist the experts or radiologists in evaluating the bone age objectively.

scholarly journals A Self-Attention Mechanism Neural Network for Detection and Diagnosis of COVID-19 from Chest X-Ray Images

2021 ◽  
Author(s):  
Bo Cheng ◽  
Wei Xiang ◽  
Ruhui Xue ◽  
Hang Yang ◽  
Laili Zhu
Keyword(s):  
Neural Network ◽  
Attention Mechanism ◽  
X Rays ◽  
Diagnostic Features ◽  
Data Set ◽  
X Ray ◽  
Difficulty Breathing ◽  
Enhancement Method ◽  
Detection And Diagnosis ◽  
Characteristic Area

Abstract The new type of coronavirus is called COVID-19. The virus can cause respiratory diseases, accompanied by cough, fever, difficulty breathing, and in severe cases, it can also cause symptoms such as pneumonia. It began to spread at the end of 2019 and has now spread to all parts of the world. The limited test kits and increasing number of cases encourage us to propose a deep learning model that can help radiologists and clinicians use chest X-rays to detect COVID-19 cases and show the diagnostic features of pneumonia. In this study, our methods are: 1) Propose a data enhancement method to increase the diversity of the data set, thereby improving the generalization performance of the network. 2) Using the deep convolutional neural network model DPN-SE, an attention mechanism is added on the basis of the DPN network, which greatly improves the performance of the network. 3) Use the lime interpretable library to mark the X-ray, the characteristic area on the medical image that is helpful for the doctor to make a diagnosis. The model we proposed can obtain better results with the least amount of data preprocessing given limited data. In general, the proposed method and model can effectively become a very useful tool for clinical practitioners and radiologists.

Automated grading of knee osteoarthritis X-ray images based on attention mechanism

2021 ◽  
Author(s):  
Yibo Feng ◽  
Jing Liu ◽  
Huan Zhang ◽  
Dawei Qiu
Keyword(s):  
Knee Osteoarthritis ◽  
Attention Mechanism ◽  
X Ray ◽  
Automated Grading

scholarly journals A Self-Attention Mechanism for COVID-19 Detection using Chest X-Ray Images

2021 ◽  
Author(s):  
Usman Muhammad ◽  
Md Ziaul Hoque ◽  
Mourad Oussalah ◽  
Anja Keskinarkaus ◽  
Tapio Seppänen ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Feature Space ◽  
Viral Disease ◽  
Attention Mechanism ◽  
Current Evidence ◽  
Great Promise ◽  
X Ray ◽  
Benchmark Datasets ◽  
Chest X Ray ◽  
Low Dimensional

<p>COVID-19 is a rapidly spreading viral disease and has affected over 100 countries worldwide. The numbers of casualties and infected cases have been escalated particularly in vulnerable states with weakened healthcare systems. Recently, reverse transcription-polymerase chain reaction (RT-PCR) is the test of choice for diagnosing COVID-19. However, current evidence suggests that COVID-19 infected patients are mostly stimulated from a lung infection after coming in contact with this virus. Therefore, chest X-ray (i.e., radiography) and chest CT can be a surrogate in some countries where PCR is not readily available. This has forced the scientific community to detect COVID-19 infection from X-ray images and recently proposed machine learning methods offer great promise for fast and accurate detection. Deep learning with convolutional neural networks (CNNs) has been successfully applied to radiological imaging for improving the accuracy of diagnosis. However, the performance remains limited due to the lack of representative X-ray images available in public benchmark datasets. To alleviate this issue, we propose an attention mechanism for data augmentation in the feature space rather than in the data space using reconstruction independent component analysis (RICA). Specifically, a unified architecture is proposed which contains a deep convolutional neural network (CNN), an attention mechanism, and a bidirectional LSTM (BiLSTM). The CNN provides the high-level features extracted at the pooling layer where the attention mechanism chooses the most relevant features and generates low-dimensional augmented features. Finally, BiLSTM is used to classify the processed sequential information. We conducted experiments on two publicly available databases to show that the proposed approach achieves the state-of-the-art results with an accuracy of 97% and 84% while being able to generate explanations. Explainability analysis has been carried out using feature visualization through PCA projection and t-SNE plots.<br></p>

scholarly journals A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Geng Hong ◽  
Xiaoyan Chen ◽  
Jianyong Chen ◽  
Miao Zhang ◽  
Yumeng Ren ◽  
...  
Keyword(s):  
Cross Validation ◽  
Ct Images ◽  
Attention Mechanism ◽  
X Ray ◽  
Multi Scale ◽  
Specificity And Sensitivity ◽  
The World ◽  
Tenfold Cross Validation ◽  
Model Size ◽  
Feature Information

AbstractCoronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread rapidly throughout the world. In this paper, a lightweight convolutional neural network (CNN) model named multi-scale gated multi-head attention depthwise separable CNN (MGMADS-CNN) is proposed, which is based on attention mechanism and depthwise separable convolution. A multi-scale gated multi-head attention mechanism is designed to extract effective feature information from the COVID-19 X-ray and CT images for classification. Moreover, the depthwise separable convolution layers are adopted as MGMADS-CNN’s backbone to reduce the model size and parameters. The LeNet-5, AlexNet, GoogLeNet, ResNet, VGGNet-16, and three MGMADS-CNN models are trained, validated and tested with tenfold cross-validation on X-ray and CT images. The results show that MGMADS-CNN with three attention layers (MGMADS-3) has achieved accuracy of 96.75% on X-ray images and 98.25% on CT images. The specificity and sensitivity are 98.06% and 96.6% on X-ray images, and 98.17% and 98.05% on CT images. The size of MGMADS-3 model is only 43.6 M bytes. In addition, the detection speed of MGMADS-3 on X-ray images and CT images are 6.09 ms and 4.23 ms for per image, respectively. It is proved that the MGMADS-3 can detect and classify COVID-19 faster with higher accuracy and efficiency.

scholarly journals Automatic Lung Segmentation Algorithm on Chest X-ray Images Based on Fusion Variational Auto-Encoder and Three-Terminal Attention Mechanism

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 814
Author(s):  
Feidao Cao ◽  
Huaici Zhao
Keyword(s):  
Automatic Segmentation ◽  
Attention Mechanism ◽  
Segmentation Algorithm ◽  
Target Area ◽  
Lung Segmentation ◽  
X Ray ◽  
Chest X Ray ◽  
Left And Right ◽  
High Level ◽  
Better Than

Automatic segmentation of the lungs in Chest X-ray images (CXRs) is a key step in the screening and diagnosis of related diseases. There are many opacities in the lungs in the CXRs of patients, which makes the lungs difficult to segment. In order to solve this problem, this paper proposes a segmentation algorithm based on U-Net. This article introduces variational auto-encoder (VAE) in each layer of the decoder-encoder. VAE can extract high-level semantic information, such as the symmetrical relationship between the left and right thoraxes in most cases. The fusion of the features of VAE and the features of convolution can improve the ability of the network to extract features. This paper proposes a three-terminal attention mechanism. The attention mechanism uses the channel and spatial attention module to automatically highlight the target area and improve the performance of lung segmentation. At the same time, the three-terminal attention mechanism uses the advanced semantics of high-scale features to improve the positioning and recognition capabilities of the attention mechanism, suppress background noise, and highlight target features. Experimental results on two different datasets show that the accuracy (ACC), recall (R), F1-Score and Jaccard values of the algorithm proposed in this paper are the highest on the two datasets, indicating that the algorithm in this paper is better than other state-of-the-art algorithms.

scholarly journals Attention-Based Transfer Learning for Efficient Pneumonia Detection in Chest X-ray Images

2021 ◽  
Vol 11 (3) ◽  
pp. 1242
Author(s):  
So-Mi Cha ◽  
Seung-Seok Lee ◽  
Bonggyun Ko
Keyword(s):  
Transfer Learning ◽  
Area Under The Curve ◽  
Attention Mechanism ◽  
Stable Model ◽  
X Rays ◽  
X Ray ◽  
Learning Framework ◽  
Computer Aided ◽  
Common Technique ◽  
Chest X Ray

Pneumonia is a form of acute respiratory infection commonly caused by germs, viruses, and fungi, and can prove fatal at any age. Chest X-rays is the most common technique for diagnosing pneumonia. There have been several attempts to apply transfer learning based on a Convolutional Neural Network to build a stable model in computer-aided diagnosis. Recently, with the appearance of an attention mechanism that automatically focuses on the critical part of the image that is crucial for the diagnosis of disease, it is possible to increase the performance of previous models. The goal of this study is to improve the accuracy of a computer-aided diagnostic approach that medical professionals can easily use as an auxiliary tool. In this paper, we proposed the attention-based transfer learning framework for efficient pneumonia detection in chest X-ray images. We collected features from three-types of pre-trained models, ResNet152, DenseNet121, ResNet18 as a role of feature extractor. We redefined the classifier for a new task and applied the attention mechanism as a feature selector. As a result, the proposed approach achieved accuracy, F-score, Area Under the Curve(AUC), precision and recall of 96.63%, 0.973, 96.03%, 96.23% and 98.46%, respectively.

scholarly journals A Self-Attention Mechanism for COVID-19 Detection using Chest X-Ray Images

2021 ◽  
Author(s):  
Usman Muhammad ◽  
Md Ziaul Hoque ◽  
Mourad Oussalah ◽  
Anja Keskinarkaus ◽  
Tapio Seppänen ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Feature Space ◽  
Viral Disease ◽  
Attention Mechanism ◽  
Current Evidence ◽  
Great Promise ◽  
X Ray ◽  
Benchmark Datasets ◽  
Chest X Ray ◽  
Low Dimensional

<p>COVID-19 is a rapidly spreading viral disease and has affected over 100 countries worldwide. The numbers of casualties and infected cases have been escalated particularly in vulnerable states with weakened healthcare systems. Recently, reverse transcription-polymerase chain reaction (RT-PCR) is the test of choice for diagnosing COVID-19. However, current evidence suggests that COVID-19 infected patients are mostly stimulated from a lung infection after coming in contact with this virus. Therefore, chest X-ray (i.e., radiography) and chest CT can be a surrogate in some countries where PCR is not readily available. This has forced the scientific community to detect COVID-19 infection from X-ray images and recently proposed machine learning methods offer great promise for fast and accurate detection. Deep learning with convolutional neural networks (CNNs) has been successfully applied to radiological imaging for improving the accuracy of diagnosis. However, the performance remains limited due to the lack of representative X-ray images available in public benchmark datasets. To alleviate this issue, we propose an attention mechanism for data augmentation in the feature space rather than in the data space using reconstruction independent component analysis (RICA). Specifically, a unified architecture is proposed which contains a deep convolutional neural network (CNN), an attention mechanism, and a bidirectional LSTM (BiLSTM). The CNN provides the high-level features extracted at the pooling layer where the attention mechanism chooses the most relevant features and generates low-dimensional augmented features. Finally, BiLSTM is used to classify the processed sequential information. We conducted experiments on two publicly available databases to show that the proposed approach achieves the state-of-the-art results with an accuracy of 97% and 84% while being able to generate explanations. Explainability analysis has been carried out using feature visualization through PCA projection and t-SNE plots.<br></p>

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