Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling

2018 ◽  
Vol 27 ◽  
pp. 57-68 ◽  
Author(s):  
Yu-Dong Zhang ◽  
Chichun Pan ◽  
Xianqing Chen ◽  
Fubin Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Rectified Linear Unit ◽  
Parametric Rectified Linear Unit ◽  
Stochastic Pooling

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 37-41
Author(s):  
Zhixian Chen ◽  
Jialin Tang ◽  
Xueyuan Gong ◽  
Qinglang Su
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Principal Component ◽  
Activation Function ◽  
Support Vector ◽  
Data Set ◽  
Novel Approach ◽  
Rectified Linear Unit ◽  
The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling

2017 ◽  
Vol 42 (1) ◽  
Author(s):  
Shui-Hua Wang ◽  
Yi-Ding Lv ◽  
Yuxiu Sui ◽  
Shuai Liu ◽  
Su-Jing Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Stochastic Pooling

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

STEMedicine ◽  
2021 ◽  
Vol 2 (8) ◽  
pp. e101
Author(s):  
Jian Wang ◽  
Dimas Lima
Keyword(s):  
Neural Network ◽  
Multiple Sclerosis ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Early Stage ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
White Matter Lesions ◽  
Learning Methods ◽  
Stochastic Pooling

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.

scholarly journals Reconfiguration layers of convolutional neural network for fundus patches classification

2021 ◽  
Vol 10 (1) ◽  
pp. 383-389
Author(s):  
Wahyudi Setiawan ◽  
Moh. Imam Utoyo ◽  
Riries Rulaningtyas
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Original Data ◽  
Max Pooling ◽  
Output Layer ◽  
Batch Normalization ◽  
Rectified Linear Unit ◽  
Input Layer ◽  
Fully Connected

Convolutional neural network (CNN) is a method of supervised deep learning. The architectures including AlexNet, VGG16, VGG19, ResNet 50, ResNet101, GoogleNet, Inception-V3, Inception ResNet-V2, and Squeezenet that have 25 to 825 layers. This study aims to simplify layers of CNN architectures and increased accuracy for fundus patches classification. Fundus patches classify two categories: normal and neovascularization. Data used for classification is MESSIDOR and Retina Image Bank that have 2,080 patches. Results show the best accuracy of 93.17% for original data and 99,33% for augmentation data using CNN 31 layers. It consists input layer, 7 convolutional layers, 7 batch normalization, 7 rectified linear unit, 6 max-pooling, fully connected layer, softmax, and output layer.

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