Diagnosis of secondary pulmonary tuberculosis by an eight-layer improved convolutional neural network with stochastic pooling and hyperparameter optimization

Zhang, Yudong; Nayak, Deepak Ranjan; Zhang, Xin; Wang, Shuihua

doi:10.1007/s12652-020-02612-9

Cited by 22 publications

(17 citation statements)

References 27 publications

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“…In the standard convolutional neural networks, pooling is an essential component after each convolution layer, which was applied to reduce the size of feature maps (FMs). SP was shown to give better performance than average pooling and max pooling in recent publications [18][19][20][21]. Recently, strided convolution (SC) is commonly used, which also can shrink the FMs [22,23].…”

Section: Improvement I: N-conv Stochastic Poolingmentioning

confidence: 99%

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

Wang

Zhang

Cheng

et al. 2021

Computational and Mathematical Methods in Medicine

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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.

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Section: Improvement I: N-conv Stochastic Poolingmentioning

confidence: 99%

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

Wang

Zhang

Cheng

et al. 2021

Computational and Mathematical Methods in Medicine

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“…The standard normalization of input A will make it concentrate near 0. If the sigmoid activation function is used, this value interval is just close to the linear transformation interval, which will weaken the nature of the nonlinear transformation of the neural network [34]. In order to make the normalization operation not have a negative impact on the network's representation ability, an additional scaling and translation transformation can be used to change the value range and represent the parameter vector of scaling and translation respectively, and they are also two learnable parameters.…”

Section: Batch Normalizationmentioning

confidence: 99%

Alzheimer’s disease diagnosis via 5-layer Convolutional Neural Network and Data Augmentation

Gao¹

2022

EAI Endorsed Transactions on e-Learning

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OBJECTIVES: Alzheimer's disease (AD) is a progressive neurodegenerative disease with insidious onset and one of the biggest challenges in geriatrics. Because the cause of the disease is unknown and there is currently no cure, AD Early diagnosis is particularly important. METHODS: In this paper, we built a 5-layer convolutional neural network based on deep learning technology. We used six data augmentation methods to increase the size of the training set. Batch normalization and dropout techniques are also used, which are respectively associated with the convolutional layer and the fully connected layer, Form convolution batch normalization (CB) and dropout fully connected (DOFC) block respectively. RESULTS: Our 5-layer CNN has shown excellent results on the training set, a sensitivity of 94.80%, a specificity of 93.98%, a precision of 94.04% and an accuracy of 94.39%, and has good performance compared with several other state-of-the-art methods. CONCLUSION: In terms of classification performance, our method performs better than 8 state-of-the-art approaches and the performance of human observers. Therefore, this proposed method is effective in the detection of Alzheimer's disease.

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“…In the second module, a bistaged feature selection technique, a guided approach using mutual information and Relief-F followed by the Dragonfly algorithm, was used for selecting the top k salient features from the CT images. Zhang et al (2020) performed a research analysis proposing an 8-layer enhanced CNN model with stochastic pooling and hyperparameter optimization. Stochastic pooling was adopted to replace normal average pooling and max pooling.…”

Section: Covid-19 Screening From Ct Scansmentioning

confidence: 99%

A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images

Bhowal

Sen

Sarkar

2021

J Ambient Intell Human Comput

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The world is still under the threat of different strains of the coronavirus and the pandemic situation is far from over. The method, that is widely used for the detection of COVID-19 is Reverse Transcription Polymerase chain reaction (RT-PCR), which is a time-consuming method and is prone to manual errors, and has poor precision. Although many nations across the globe have begun the mass immunization procedure, the COVID-19 vaccine will take a long time to reach everyone. The application of artificial intelligence (AI) and computer-aided diagnosis (CAD) has been used in the domain of medical imaging for a long period. It is quite evident that the use of CAD in the detection of COVID-19 is inevitable. The main objective of this paper is to use convolutional neural network (CNN) and a novel feature selection technique to analyze Chest X-Ray (CXR) images for the detection of COVID-19. We propose a novel two-tier feature selection method, which increases the accuracy of the overall classification model used for screening COVID-19 CXRs. Filter feature selection models are often more effective than wrapper methods as wrapper methods tend to be computationally more expensive and are not useful for large datasets dealing with a large number of features. However, most filter methods do not take into consideration how a group of features would work together, rather they just look at the features individually and decide on a score. We have used approximate Shapley value, a concept of Coalition game theory, to deal with this problem. Further, in the case of a large dataset, it is important to work with shorter embeddings of the features. We have used CUR decomposition and Nystrom sampling to further reduce the feature space. To check the efficacy of this two-tier feature selection method, we have applied it to the features extracted by three standard deep learning models, namely VGG16 , Xception and InceptionV3 , where the features have been extracted from the CXR images of COVID-19 datasets and we have found that the selection procedure works quite well for the features extracted by Xception and InceptionV3 . The source code of this work is available at https://github.com/subhankar01/covidfs-aihc .

show abstract

Diagnosis of secondary pulmonary tuberculosis by an eight-layer improved convolutional neural network with stochastic pooling and hyperparameter optimization

Cited by 22 publications

References 27 publications

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

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

Alzheimer’s disease diagnosis via 5-layer Convolutional Neural Network and Data Augmentation

A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images

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