Forward Stability of ResNet and Its Variants

Zhang, Linan; Schaeffer, Hayden

doi:10.1007/s10851-019-00922-y

Cited by 46 publications

(51 citation statements)

References 30 publications

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“…The difference between the ResNet and the ordinary network is that the jump connection is introduced, which can help the information of the previous residual block flow into the next residual block without obstruction. The problem of vanishing gradient and degradation caused by too deep a network is avoided [ 58 , 59 ].…”

Section: Methodsmentioning

confidence: 99%

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An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer’s Disease

Sun

Wang

Liu

2021

Sensors

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The early diagnosis of Alzheimer’s disease (AD) can allow patients to take preventive measures before irreversible brain damage occurs. It can be seen from cross-sectional imaging studies of AD that the features of the lesion areas in AD patients, as observed by magnetic resonance imaging (MRI), show significant variation, and these features are distributed throughout the image space. Since the convolutional layer of the general convolutional neural network (CNN) cannot satisfactorily extract long-distance correlation in the feature space, a deep residual network (ResNet) model, based on spatial transformer networks (STN) and the non-local attention mechanism, is proposed in this study for the early diagnosis of AD. In this ResNet model, a new Mish activation function is selected in the ResNet-50 backbone to replace the Relu function, STN is introduced between the input layer and the improved ResNet-50 backbone, and a non-local attention mechanism is introduced between the fourth and the fifth stages of the improved ResNet-50 backbone. This ResNet model can extract more information from the layers by deepening the network structure through deep ResNet. The introduced STN can transform the spatial information in MRI images of Alzheimer’s patients into another space and retain the key information. The introduced non-local attention mechanism can find the relationship between the lesion areas and normal areas in the feature space. This model can solve the problem of local information loss in traditional CNN and can extract the long-distance correlation in feature space. The proposed method was validated using the ADNI (Alzheimer’s disease neuroimaging initiative) experimental dataset, and compared with several models. The experimental results show that the classification accuracy of the algorithm proposed in this study can reach 97.1%, the macro precision can reach 95.5%, the macro recall can reach 95.3%, and the macro F1 value can reach 95.4%. The proposed model is more effective than other algorithms.

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Section: Methodsmentioning

confidence: 99%

“…The introduction of more Mish activation functions can improve the representation ability of ResNet. The bottleneck residuals module of different layers for the ResNet-50 architecture is expressed in Figure 2 [ 58 , 59 , 60 ].…”

Section: Methodsmentioning

confidence: 99%

An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer’s Disease

Sun

Wang

Liu

2021

Sensors

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“…However, this increase can also raise several problems, including the vanishing of information between the distant layers during training. Recent work has shown that CNNs can be substantially deeper, more accurate, and efficient to train, if they contain shortcut connections between distant layers [36].…”

Section: The Densenet Neural Architecturementioning

confidence: 99%

Assessing flood severity from georeferenced photos

Pereira

Monteiro

Estima

et al. 2019

Proceedings of the 13th Workshop on Geographic Information Retrieval

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The use of georeferenced social media data in disaster and crisis management is increasing rapidly. Particularly in connection to flooding events, georeferenced images shared by citizens can provide situational awareness to emergency responders, as well as assistance to financial loss assessment, giving information that would otherwise be very hard to collect through conventional sensors or remote sensing products. Moreover, recent advances in computer vision and deep learning can perhaps support the automated analysis of these data. In this paper, focusing on ground-level images taken by humans during flooding events, we evaluate the use of deep convolutional neural networks for (i) discriminating images showing direct evidence of a flood, and (ii) estimating the severity of the flooding event. Considering distinct datasets (i.e., the European Flood 2013 dataset, and data from different editions of the MediaEval Multimedia Satellite Task), we specifically evaluated models based on the DenseNet and EfficientNet neural network architectures, concluding that these models for image classification can achieve a very high accuracy on both tasks. CCS CONCEPTS • Computing methodologies → Computer vision tasks; Neural networks; Visual content-based indexing and retrieval; Supervised learning.

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“…Otherwise, the results would not be acceptable. The well-known networks, for instance, AlexNet [25], variants of ResNet [26], VGG [27], GoogLeNet [28], EfficientNet [29], and DensNet [30] prove themselves to be powerful in many applications; however, a major drawback to them is that they usually require significant training time, causing a high cost in real-world applications [31].…”

Section: Introductionmentioning

confidence: 99%

An Accuracy vs. Complexity Comparison of Deep Learning Architectures for the Detection of COVID-19 Disease

et al. 2021

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In parallel with the vast medical research on clinical treatment of COVID-19, an important action to have the disease completely under control is to carefully monitor the patients. What the detection of COVID-19 relies on most is the viral tests, however, the study of X-rays is helpful due to the ease of availability. There are various studies that employ Deep Learning (DL) paradigms, aiming at reinforcing the radiography-based recognition of lung infection by COVID-19. In this regard, we make a comparison of the noteworthy approaches devoted to the binary classification of infected images by using DL techniques, then we also propose a variant of a convolutional neural network (CNN) with optimized parameters, which performs very well on a recent dataset of COVID-19. The proposed model’s effectiveness is demonstrated to be of considerable importance due to its uncomplicated design, in contrast to other presented models. In our approach, we randomly put several images of the utilized dataset aside as a hold out set; the model detects most of the COVID-19 X-rays correctly, with an excellent overall accuracy of 99.8%. In addition, the significance of the results obtained by testing different datasets of diverse characteristics (which, more specifically, are not used in the training process) demonstrates the effectiveness of the proposed approach in terms of an accuracy up to 93%.

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Forward Stability of ResNet and Its Variants

Cited by 46 publications

References 30 publications

An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer’s Disease

An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer’s Disease

Assessing flood severity from georeferenced photos

An Accuracy vs. Complexity Comparison of Deep Learning Architectures for the Detection of COVID-19 Disease

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