Progressive U-Net residual network for computed tomography images super-resolution in the screening of COVID-19

Qiu, Defu; Cheng, Yuhu; Wang, Xuesong

doi:10.1080/16878507.2021.1973760

Cited by 18 publications

(11 citation statements)

References 36 publications

(52 reference statements)

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“…Likewise with the grouping of the testing dataset. (Qiu et al, 2021). The entire network in figure 2 on the left consists of a parent module, four residual modules, and a fully connected neural network layer.…”

Section: Methodsmentioning

confidence: 99%

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Compare VGG19, ResNet50, Inception-V3 for Review Food Rating

Andrew

Santoso

2022

SinkrOn

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The food industry is undergoing a phase of very good improvement, where business actors are experiencing very rapid growth. Creative ideas are many and creative on several social media. When an online business is growing rapidly, many managers in the food sector market their products through online media. So it is quite easy for customers to place orders via mobile. Especially during the COVID-19 pandemic, where a ban on gatherings has become a government recommendation for many food business actors to sell online. Since then, almost all food industry players have made their sales online. There are many advantages of doing business online. The food served is in the form of pictures that attract market visitors so that it can create its own charm. Food is just a click away to order, and the order comes. No need to queue and everything has been delivered to the ordered goods. After the ordered goods arrive, the customer reviews the food or drink. Because customer reviews are the result of customer ratings. The result of the review is one of the sentiment analyses, which in this study is in the form of a review of the images available on the display marketplace. The method used is Convolutional Neural Network. The dataset will be extracted features and classifications. The research will do a comparison using VGG19, ResNet50, and Inception-V3. Where the accuracy of VGG19 = 96.86; Resnet50 : 97.29; Inception_v3 : 97.57.

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Section: Methodsmentioning

confidence: 99%

“…Fig 4. Resnet50 Architecture Source : (Wang et al, 2021) Figure 4. shows the detailed architecture of the ResNet50-vd network(ABDULFATTAH et al, 2021),(Qiu et al, 2021). The entire network in figure2on the left consists of a parent module, four residual modules, and a fully connected neural network layer.…”

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confidence: 99%

Compare VGG19, ResNet50, Inception-V3 for Review Food Rating

Andrew

Santoso

2022

SinkrOn

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“…Most of the features are extracted manually, which requires a certain degree of expertise. In recent years, based on deep learning, the method of automatically learning features from EEG signals has become popular, and there are also many methods of automatic sleep stage classification based on deep learning [ 14 ].…”

Section: Related Workmentioning

confidence: 99%

Propofol Anesthesia Depth Monitoring Based on Self-Attention and Residual Structure Convolutional Neural Network

Wang

Zhang

Fan

et al. 2022

Computational and Mathematical Methods in Medicine

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Background and Objective. To study the new method of selecting CNN+EEG index values, based on self-attention and residual structure of convolutional neural network, to deeply monitor propofol anesthesia. Methods. We compare nine index values, select CNN+EEG, which has good correlation with BIS index, as an anesthesia state observation index to identify the parameters of the model, and establish a model based on self-attention and dual resistructure convolutional neural network. The data of 93 groups of patients were selected and randomly grouped into three parts: training set, validation set, and test set, and compared the best and worst results predicted by BIS. Result. The best result is that the model’s accuracy of predicting BLS on the test set has an overall upward trend, eventually reaching more than 90%. The overall error shows a gradual decrease and eventually approaches zero. The worst result is that the model’s accuracy of predicting BIS on the test set has an overall upward trend. The accuracy rate is relatively stable without major fluctuations, but the final accuracy rate is above 70%. Conclusion. The prediction of BIS indicators by the deep learning method CNN algorithm shows good results in statistics.

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“…This network is 8 times deeper than the VGG network but the complexity is still lower than the VGG network. In 2015, the network won first place in the ILSVRC (ImageNet Large Scale Visual Recognition Challenge) and COCO competitions in terms of image classification, detection and segmentation on COCO and ImageNet datasets (Niswati et al, 2021) Figure 2 shows the detailed architecture of the ResNet50-vd network (ABDULFATTAH et al, 2021), (Qiu et al, 2021). The entire network in figure 2 on the left consists of a parent module, four residual modules, and a fully connected neural network layer.…”

Section: Convolutional Neural Network Classifiermentioning

confidence: 99%

Heavy-loaded Vehicles Detection Model Testing using Synthetic Dataset

Karjadi

Wedha

Santoso

2022

SinkrOn

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Currently, many roads in Indonesia are damaged. This is due to the presence of large vehicles and large loads that often pass. The more omissions are carried out, the more damaged and severe the road is. The central government and local governments often carry out road repairs, but this problem is often a problem. Damaged roads are indeed many factors, one of which is the road load. The road load is caused by the number of vehicles that carry more than the specified capacity. There are many methods used to monitor roads for road damage. The weighing post is a means used by the government in conducting surveillance. This research is not a proposal to monitor the road, but this is only to create a model for the purpose of detecting heavily or lightly loaded vehicles. This research is to classify using Convolutional Neural Network (CNN) with pre-trained Resnet50. The model generated from the Convolutional Neural Network training process reaches above 90%. Generate Image deep learning algorithms such as the Generative Adversarial Network currently generate a lot of synthetic images. The testing dataset that will be used is generated from style transfer. The model is tested using a testing dataset from the generated style transfer. Style transfer is a method of generating images by combining image content with image styles. The model is pretty good at around 92% for training and 88% for testing, can it detect image style transfer? The Convolutional Neural Network model is said to be good if it is able to recognize the image correctly, considering that the accuracy of the model is very good. One of the reasons why the training model is good but still makes errors during testing, then the image dataset is overfitting

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Progressive U-Net residual network for computed tomography images super-resolution in the screening of COVID-19

Cited by 18 publications

References 36 publications

Compare VGG19, ResNet50, Inception-V3 for Review Food Rating

Compare VGG19, ResNet50, Inception-V3 for Review Food Rating

Propofol Anesthesia Depth Monitoring Based on Self-Attention and Residual Structure Convolutional Neural Network

Heavy-loaded Vehicles Detection Model Testing using Synthetic Dataset

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