Cerebral micro‐bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling

2019 ◽  
Vol 32 (1) ◽  
Author(s):  
Shuihua Wang ◽  
Junding Sun ◽  
Irfan Mehmood ◽  
Chichun Pan ◽  
Yi Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Stochastic Pooling

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 A seven-layer convolutional neural network for chest CT based COVID-19 diagnosis using stochastic pooling

2020 ◽  
pp. 1-1
Author(s):  
Yu-Dong Zhang ◽  
Suresh Chandra Satapathy ◽  
Li-Yao Zhu ◽  
Juan Manuel Gorriz ◽  
Shui-Hua Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Chest Ct ◽  
Stochastic Pooling

scholarly journals SOSPCNN: Structurally Optimized Stochastic Pooling Convolutional Neural Network for Tetralogy of Fallot Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Shui-Hua Wang ◽  
Kaihong Wu ◽  
Tianshu Chu ◽  
Steven L. Fernandes ◽  
Qinghua Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computed Tomography ◽  
Structural Optimization ◽  
Convolutional Neural Network ◽  
Tetralogy Of Fallot ◽  
Data Augmentation ◽  
State Of The Art ◽  
Stochastic Pooling ◽  
Better Than

Aim. This study proposes a new artificial intelligence model based on cardiovascular computed tomography for more efficient and precise recognition of Tetralogy of Fallot (TOF). Methods. Our model is a structurally optimized stochastic pooling convolutional neural network (SOSPCNN), which combines stochastic pooling, structural optimization, and convolutional neural network. In addition, multiple-way data augmentation is used to overcome overfitting. Grad-CAM is employed to provide explainability to the proposed SOSPCNN model. Meanwhile, both desktop and web apps are developed based on this SOSPCNN model. Results. The results on ten runs of 10-fold crossvalidation show that our SOSPCNN model yields a sensitivity of 92.25 ± 2.19 , a specificity of 92.75 ± 2.49 , a precision of 92.79 ± 2.29 , an accuracy of 92.50 ± 1.18 , an F1 score of 92.48 ± 1.17 , an MCC of 85.06 ± 2.38 , an FMI of 92.50 ± 1.17 , and an AUC of 0.9587. Conclusion. The SOSPCNN method performed better than three state-of-the-art TOF recognition approaches.

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