scholarly journals Rumor Classification Model Based on Deep Convolutional Neural Networks

2018 ◽  
Author(s):  
Rui SUN ◽  
Yong ZHONG ◽  
Wan-bo LUO
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Classification Model ◽  
Deep Convolutional Neural Networks ◽  
Model Based

Skin Cancer Classification with Deep Convolutional Neural Networks

2020 ◽  
Vol 10 (7) ◽  
pp. 1707-1713 ◽  
Author(s):  
Mingang Chen ◽  
Wenjie Chen ◽  
Wei Chen ◽  
Lizhi Cai ◽  
Gang Chai
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Skin Diseases ◽  
Survival Rates ◽  
Skin Lesions ◽  
Classification Model ◽  
Deep Convolutional Neural Networks ◽  
Image Preprocessing ◽  
Skin Cancers

Skin cancers are one of the most common cancers in the world. Early detections and treatments of skin cancers can greatly improve the survival rates of patients. In this paper, a skin lesions classification system is developed with deep convolutional neural networks of ResNet50, which may help dermatologists to recognize skin cancers earlier. We utilize the ResNet50 as a pre-trained model. Then, by transfer learning, it is trained on our skin lesions dataset. Image preprocessing and dataset balancing methods are used to increase the accuracy of the classification model. In classification of skin diseases, our model achieves an overall accuracy of 83.74% on nine-class skin lesions. The experimental results show an impressive effect of the ResNet50 model in finegrained skin lesions classification and skin cancers recognition.

scholarly journals A Novel Way to Generate Adversarial Network Traffic Samples against Network Traffic Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yongjin Hu ◽  
Jin Tian ◽  
Jun Ma
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Network Traffic ◽  
Classification Model ◽  
Traffic Classification ◽  
Deep Convolutional Neural Networks ◽  
Adversarial Network ◽  
Network Traffic Classification ◽  
Normal Network

Network traffic classification technologies could be used by attackers to implement network monitoring and then launch traffic analysis attacks or website fingerprint attacks. In order to prevent such attacks, a novel way to generate adversarial samples of network traffic from the perspective of the defender is proposed. By adding perturbation to the normal network traffic, a kind of adversarial network traffic is formed, which will cause misclassification when the attackers are implementing network traffic classification with deep convolutional neural networks (CNN) as a classification model. The paper uses the concept of adversarial samples in image recognition for reference to the field of network traffic classification and chooses several different methods to generate adversarial samples of network traffic. The experiment, in which the LeNet-5 CNN is selected as a classification model used by attackers and Vgg16 CNN is selected as the model to test the transferability of the adversarial network traffic generated, shows the effect of the adversarial network traffic samples.

scholarly journals Automated interpretation of the coronary angioscopy with deep convolutional neural networks

2019 ◽  
Author(s):  
Toru Miyoshi ◽  
Akinori Higaki ◽  
Hideo Kawakami ◽  
Osamu Yamaguchi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Prediction Models ◽  
Expert Knowledge ◽  
Classification Model ◽  
Generative Adversarial Networks ◽  
Deep Convolutional Neural Networks ◽  
Coronary Angioscopy ◽  
R Value ◽  
Grade Average

AbstractBackgroundCoronary angioscopy (CAS) is a useful modality to assess atherosclerotic changes, but interpretation of the images requires expert knowledge. Deep convolutional neural networks (DCNN) can be used for diagnostic prediction and image synthesis.Methods107 images from 47 patients, who underwent coronary angioscopy in our hospital between 2014 and 2017, and 864 images, selected from 142 MEDLINE-indexed articles published between 2000 and 2019, were analyzed. First, we developed a prediction model for the angioscopic findings. Next, we made a generative adversarial networks (GAN) model to simulate the CAS images. Finally, we tried to control the output images according to the angioscopic findings with conditional GAN architecture.ResultsFor both yellow color (YC) grade and neointimal coverage (NC) grade, we could observe strong correlations between the true grades and the predicted values (YC grade, average r value = 0.80 ± 0.02, p-value <0.001; NC grade, average r value = 0.73 ± 0.02, p < 0.001). The binary classification model for the red thrombus yielded 0.71 ± 0.03 F1-score and the area under the ROC curve (AUC) was 0.91 ± 0.02. The standard GAN model could generate realistic CAS images (average Inception score = 3.57 ± 0.06). GAN-based data augmentation improved the performance of the prediction models. In the conditional GAN model, there were significant correlations between given values and the expert’s diagnosis in YC grade and NC grade.ConclusionDCNN is useful in both predictive and generative modeling that can help develop the diagnostic support system for CAS.

scholarly journals Automated interpretation of the coronary angioscopy with deep convolutional neural networks

Open Heart ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. e001177 ◽  
Author(s):  
Toru Miyoshi ◽  
Akinori Higaki ◽  
Hideo Kawakami ◽  
Osamu Yamaguchi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Prediction Models ◽  
Expert Knowledge ◽  
Classification Model ◽  
Generative Adversarial Networks ◽  
Deep Convolutional Neural Networks ◽  
Yellow Colour ◽  
Coronary Angioscopy ◽  
Grade Average

BackgroundCoronary angioscopy (CAS) is a useful modality to assess atherosclerotic changes, but interpretation of the images requires expert knowledge. Deep convolutional neural networks (DCNN) can be used for diagnostic prediction and image synthesis.Methods107 images from 47 patients, who underwent CAS in our hospital between 2014 and 2017, and 864 images, selected from 142 MEDLINE-indexed articles published between 2000 and 2019, were analysed. First, we developed a prediction model for the angioscopic findings. Next, we made a generative adversarial networks (GAN) model to simulate the CAS images. Finally, we tried to control the output images according to the angioscopic findings with conditional GAN architecture.ResultsFor both yellow colour (YC) grade and neointimal coverage (NC) grade, we could observe strong correlations between the true grades and the predicted values (YC grade, average r=0.80±0.02, p<0.001; NC grade, average r=0.73±0.02, p<0.001). The binary classification model for the red thrombus yielded 0.71±0.03 F1-score and the area under the receiver operator characteristic curve was 0.91±0.02. The standard GAN model could generate realistic CAS images (average Inception score=3.57±0.06). GAN-based data augmentation improved the performance of the prediction models. In the conditional GAN model, there were significant correlations between given values and the expert’s diagnosis in YC grade but not in NC grade.ConclusionDCNN is useful in both predictive and generative modelling that can help develop the diagnostic support system for CAS.

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