DSHPoolF: deep supervised hashing based on selective pool feature map for image retrieval

Arulmozhi, Parvathy; Abirami, S.

doi:10.1007/s00371-020-01993-4

Cited by 7 publications

(3 citation statements)

References 55 publications

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“…As one the most commonly-used noises, Gaussian noise is often added to raw images in data augmentation. Mark z as the gray level, z obeys the probability density function as follow: (16) where μ represents the mean gray value, and σ means standard deviation of z. Speckle noise. As a granular interference, speckle noise naturally occurs in radar or ultrasound images.…”

Section: Noise-based Methodsmentioning

confidence: 99%

“…Pooling layer, also named as subsampling or downsampling, is often used behind of convolutional layer in a classic architecture of CNN [15]. Its main purposes include reducing feature dimension of convolutional layer output [16], suppressing noise, reducing quantity of parameters and computation cost, and dampening overfitting [17].…”

Section: Convolutional Neural Network Pooling Layermentioning

confidence: 99%

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Multiple Sclerosis Detection via 6-layer Stochastic Pooling Convolutional Neural Network and Multiple-way Data Augmentation

Wang

Lima

2021

STEMedicine

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Multiple sclerosis is one of most widespread autoimmune neuroinflammatory diseases which mainly damages body function such as movement, sensation, and vision. Despite of conventional clinical presentation, brain magnetic resonance imaging of white matter lesions is often applied to diagnose multiple sclerosis at the early stage. In this article, we proposed a 6-layer stochastic pooling convolutional neural network with multiple-way data augmentation for multiple sclerosis detection in brain MRI images. Our approach does not demand hand-crafted features unlike those traditional machine learning methods. Via application of stochastic pooling and multiple-way data augmentation, our 6-layer CNN achieved equivalent performance against those deep learning methods which consist of so many layers and parameters that ordinarily bring difficulty to training. The results showed that this 6-layer CNN obtained a sensitivity of 95.98±0.46%, a specificity of 95.67±0.92%, and an accuracy of 95.82±0.58%. According to comparison experiments, our results are better than state-of-the-art approaches. Further, we also conducted ablation experiments to examine the contribution of stochastic pooling and multiple-way data augmentation to the original CNN model. The contrast experiments revealed that our scheme of stochastic pooling and multiple-way data augmentation enhanced the original 6-layer CNN model compared to those using maximum pooling or average pooling and inadequate data augmentation.

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Section: Noise-based Methodsmentioning

confidence: 99%

Section: Convolutional Neural Network Pooling Layermentioning

confidence: 99%

Multiple Sclerosis Detection via 6-layer Stochastic Pooling Convolutional Neural Network and Multiple-way Data Augmentation

Wang

Lima

2021

STEMedicine

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“…The pooling layer is a feature mapping layer. After adding bias, a new feature map is obtained in the pooling layer through a nonlinear function [30]. The functions of pooling are as follows: (i) reducing the size of the characteristic diagram and simplifying the computational complexity of the network; (ii) feature compression to extract the main features.…”

Section: Cnnmentioning

confidence: 99%

Fault Diagnosis Technology for Ship Electrical Power System

Sun

et al. 2022

Energies

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This paper proposes a fault diagnosis method for ship electrical power systems on the basis of an improved convolutional neural network (CNN) to support normal ship operation. First, according to the mathematical model of the ship electrical power system, the simulation model of the ship electrical power system is built using the MATLAB/Simulink simulation software platform in order to understand the normal working state and fault state of the generator and load in the power system. Then, the model is simulated to generate the fault response curve, and the picture dataset of the network model is obtained. Second, a CNN fault diagnosis model is designed using TensorFlow, an open-source tool for deep learning. Finally, network model training is performed, and the optimal diagnosis results of the ship electrical power system are obtained to realize structural parameter optimization and diagnosis. The diagnosis results show that the established simulation model and improved CNN can provide support for fault diagnosis of the ship electrical power system, improve the operation stability and safety of the ship electrical power system, and ensure safety of the crew.

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