scholarly journals PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Shui-Hua Wang ◽  
Yin Zhang ◽  
Xiaochun Cheng ◽  
Xin Zhang ◽  
Yu-Dong Zhang
Keyword(s):  
Neural Network ◽  
Pulmonary Tuberculosis ◽  
Healthy Subjects ◽  
Data Augmentation ◽  
State Of The Art ◽  
Accurate Diagnosis ◽  
Test Set ◽  
The World ◽  
Stochastic Pooling ◽  
Better Than

Aim. COVID-19 has caused large death tolls all over the world. Accurate diagnosis is of significant importance for early treatment. Methods. In this study, we proposed a novel PSSPNN model for classification between COVID-19, secondary pulmonary tuberculosis, community-captured pneumonia, and healthy subjects. PSSPNN entails five improvements: we first proposed the n-conv stochastic pooling module. Second, a novel stochastic pooling neural network was proposed. Third, PatchShuffle was introduced as a regularization term. Fourth, an improved multiple-way data augmentation was used. Fifth, Grad-CAM was utilized to interpret our AI model. Results. The 10 runs with random seed on the test set showed our algorithm achieved a microaveraged F1 score of 95.79%. Moreover, our method is better than nine state-of-the-art approaches. Conclusion. This proposed PSSPNN will help assist radiologists to make diagnosis more quickly and accurately on COVID-19 cases.

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.

scholarly journals Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

2021 ◽  
Vol 9 ◽  
Author(s):  
Shui-Hua Wang ◽  
Suresh Chandra Satapathy ◽  
Donovan Anderson ◽  
Shi-Xin Chen ◽  
Yu-Dong Zhang
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
State Of The Art ◽  
Model Averaging ◽  
Community Acquired Pneumonia ◽  
Test Set ◽  
Max Pooling ◽  
L2 Norm ◽  
Healthy Control ◽  
Novel Model

Aim: Coronavirus disease 2019 (COVID-19) is a form of disease triggered by a new strain of coronavirus. This paper proposes a novel model termed “deep fractional max pooling neural network (DFMPNN)” to diagnose COVID-19 more efficiently.Methods: This 12-layer DFMPNN replaces max pooling (MP) and average pooling (AP) in ordinary neural networks with the help of a novel pooling method called “fractional max-pooling” (FMP). In addition, multiple-way data augmentation (DA) is employed to reduce overfitting. Model averaging (MA) is used to reduce randomness.Results: We ran our algorithm on a four-category dataset that contained COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis (SPT), and healthy control (HC). The 10 runs on the test set show that the micro-averaged F1 (MAF) score of our DFMPNN is 95.88%.Discussions: This proposed DFMPNN is superior to 10 state-of-the-art models. Besides, FMP outperforms traditional MP, AP, and L2-norm pooling (L2P).

scholarly journals PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis

2021 ◽  
Vol 9 ◽  
Author(s):  
Shui-Hua Wang ◽  
Ziquan Zhu ◽  
Yu-Dong Zhang
Keyword(s):  
Neural Network ◽  
Infectious Disease ◽  
Convolutional Neural Network ◽  
Loss Function ◽  
Data Augmentation ◽  
State Of The Art ◽  
Diagnosis System ◽  
Backbone Network ◽  
The Mean ◽  
Better Than

Objective: COVID-19 is a sort of infectious disease caused by a new strain of coronavirus. This study aims to develop a more accurate COVID-19 diagnosis system.Methods: First, the n-conv module (nCM) is introduced. Then we built a 12-layer convolutional neural network (12l-CNN) as the backbone network. Afterwards, PatchShuffle was introduced to integrate with 12l-CNN as a regularization term of the loss function. Our model was named PSCNN. Moreover, multiple-way data augmentation and Grad-CAM are employed to avoid overfitting and locating lung lesions.Results: The mean and standard variation values of the seven measures of our model were 95.28 ± 1.03 (sensitivity), 95.78 ± 0.87 (specificity), 95.76 ± 0.86 (precision), 95.53 ± 0.83 (accuracy), 95.52 ± 0.83 (F1 score), 91.7 ± 1.65 (MCC), and 95.52 ± 0.83 (FMI).Conclusion: Our PSCNN is better than 10 state-of-the-art models. Further, we validate the optimal hyperparameters in our model and demonstrate the effectiveness of PatchShuffle.

Tomato pest classification using deep convolutional neural network with transfer learning, fine tuning and scratch learning

2021 ◽  
pp. 1-10
Author(s):  
Gayatri Pattnaik ◽  
Vimal K. Shrivastava ◽  
K. Parvathi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Data Augmentation ◽  
State Of The Art ◽  
Deep Convolutional Neural Network ◽  
Fine Tuning ◽  
Tomato Plants ◽  
Random Weights

Pests are major threat to economic growth of a country. Application of pesticide is the easiest way to control the pest infection. However, excessive utilization of pesticide is hazardous to environment. The recent advances in deep learning have paved the way for early detection and improved classification of pest in tomato plants which will benefit the farmers. This paper presents a comprehensive analysis of 11 state-of-the-art deep convolutional neural network (CNN) models with three configurations: transfers learning, fine-tuning and scratch learning. The training in transfer learning and fine tuning initiates from pre-trained weights whereas random weights are used in case of scratch learning. In addition, the concept of data augmentation has been explored to improve the performance. Our dataset consists of 859 tomato pest images from 10 categories. The results demonstrate that the highest classification accuracy of 94.87% has been achieved in the transfer learning approach by DenseNet201 model with data augmentation.

DSSAE: Deep Stacked Sparse Autoencoder Analytical Model for COVID-19 Diagnosis by Fractional Fourier Entropy

2022 ◽  
Vol 13 (1) ◽  
pp. 1-20
Author(s):  
Shui-Hua Wang ◽  
Xin Zhang ◽  
Yu-Dong Zhang
Keyword(s):  
Data Augmentation ◽  
State Of The Art ◽  
Community Acquired Pneumonia ◽  
Chest Ct ◽  
Two Dimensional ◽  
The World ◽  
Healthy Control ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder ◽  
Fractional Fourier Entropy

( Aim ) COVID-19 has caused more than 2.28 million deaths till 4/Feb/2021 while it is still spreading across the world. This study proposed a novel artificial intelligence model to diagnose COVID-19 based on chest CT images. ( Methods ) First, the two-dimensional fractional Fourier entropy was used to extract features. Second, a custom deep stacked sparse autoencoder (DSSAE) model was created to serve as the classifier. Third, an improved multiple-way data augmentation was proposed to resist overfitting. ( Results ) Our DSSAE model obtains a micro-averaged F1 score of 92.32% in handling a four-class problem (COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis, and healthy control). ( Conclusion ) Our method outperforms 10 state-of-the-art approaches.

Oversampling Based on Data Augmentation in Convolutional Neural Network for Silicon Wafer Defect Classification

2020 ◽  
Author(s):  
Uzma Batool ◽  
Mohd Ibrahim Shapiai ◽  
Nordinah Ismail ◽  
Hilman Fauzi ◽  
Syahrizal Salleh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Silicon Wafer ◽  
Data Augmentation ◽  
Imbalanced Data ◽  
Training Data ◽  
Defect Classification ◽  
Learning Method ◽  
Test Set

Silicon wafer defect data collected from fabrication facilities is intrinsically imbalanced because of the variable frequencies of defect types. Frequently occurring types will have more influence on the classification predictions if a model gets trained on such skewed data. A fair classifier for such imbalanced data requires a mechanism to deal with type imbalance in order to avoid biased results. This study has proposed a convolutional neural network for wafer map defect classification, employing oversampling as an imbalance addressing technique. To have an equal participation of all classes in the classifier’s training, data augmentation has been employed, generating more samples in minor classes. The proposed deep learning method has been evaluated on a real wafer map defect dataset and its classification results on the test set returned a 97.91% accuracy. The results were compared with another deep learning based auto-encoder model demonstrating the proposed method, a potential approach for silicon wafer defect classification that needs to be investigated further for its robustness.

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 AI Feynman: A physics-inspired method for symbolic regression

2020 ◽  
Vol 6 (16) ◽  
pp. eaay2631 ◽  
Author(s):  
Silviu-Marian Udrescu ◽  
Max Tegmark
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Success Rate ◽  
Unknown Function ◽  
State Of The Art ◽  
Practical Interest ◽  
Symbolic Regression ◽  
The State ◽  
Np Hard ◽  
Test Set

A core challenge for both physics and artificial intelligence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of practical interest often exhibit symmetries, separability, compositionality, and other simplifying properties. In this spirit, we develop a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques. We apply it to 100 equations from the Feynman Lectures on Physics, and it discovers all of them, while previous publicly available software cracks only 71; for a more difficult physics-based test set, we improve the state-of-the-art success rate from 15 to 90%.

scholarly journals Sentiment Classification Using Convolutional Neural Networks

2019 ◽  
Vol 9 (11) ◽  
pp. 2347 ◽  
Author(s):  
Hannah Kim ◽  
Young-Seob Jeong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Text Classification ◽  
State Of The Art ◽  
Sentiment Classification ◽  
Learning Models ◽  
Text Data ◽  
Textual Data ◽  
Better Than

As the number of textual data is exponentially increasing, it becomes more important to develop models to analyze the text data automatically. The texts may contain various labels such as gender, age, country, sentiment, and so forth. Using such labels may bring benefits to some industrial fields, so many studies of text classification have appeared. Recently, the Convolutional Neural Network (CNN) has been adopted for the task of text classification and has shown quite successful results. In this paper, we propose convolutional neural networks for the task of sentiment classification. Through experiments with three well-known datasets, we show that employing consecutive convolutional layers is effective for relatively longer texts, and our networks are better than other state-of-the-art deep learning models.

Classification of Alzheimer’s Disease via Eight-Layer Convolutional Neural Network with Batch Normalization and Dropout Techniques

2020 ◽  
Vol 10 (5) ◽  
pp. 1040-1048 ◽  
Author(s):  
Xianwei Jiang ◽  
Liang Chang ◽  
Yu-Dong Zhang
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
State Of The Art ◽  
Training Dataset ◽  
Batch Normalization ◽  
Heavy Burden

More than 35 million patients are suffering from Alzheimer’s disease and this number is growing, which puts a heavy burden on countries around the world. Early detection is of benefit, in which the deep learning can aid AD identification effectively and gain ideal results. A novel eight-layer convolutional neural network with batch normalization and dropout techniques for classification of Alzheimer’s disease was proposed. After data augmentation, the training dataset contained 7399 AD patient and 7399 HC subjects. Our eight-layer CNN-BN-DO-DA method yielded a sensitivity of 97.77%, a specificity of 97.76%, a precision of 97.79%, an accuracy of 97.76%, a F1 of 97.76%, and a MCC of 95.56% on the test set, which achieved the best performance in seven state-of-the-art approaches. The results strongly demonstrate that this method can effectively assist the clinical diagnosis of Alzheimer’s disease.

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