Denoising and deblurring gold immunochromatographic strip images via gradient projection algorithms

Zeng, Nianyin; Zhang, Hong; Li, Yurong; Liang, Jinling; Dobaie, Abdullah M.

doi:10.1016/j.neucom.2017.03.056

Cited by 60 publications

(36 citation statements)

References 23 publications

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“…Pathak et al (38) trains an encoder-decoder and combines adversarial network loss to predict the missing portion according to the context pixel and structural semantics of the missing area. The network is able to obtain a reasonable image structure and can quickly and accurately evaluate the repair results (45)(46)(47). Because GAN's generator and discriminator can be any form of neural network, different network architectures can be selected for solving different problems.…”

Section: Application Of Gan In Image Processingmentioning

confidence: 99%

Generative Adversarial Networks and Its Applications in Biomedical Informatics

Lan

You

Zhang

et al. 2020

Front. Public Health

Self Cite

169

104

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The basic Generative Adversarial Networks (GAN) model is composed of the input vector, generator, and discriminator. Among them, the generator and discriminator are implicit function expressions, usually implemented by deep neural networks. GAN can learn the generative model of any data distribution through adversarial methods with excellent performance. It has been widely applied to different areas since it was proposed in 2014. In this review, we introduced the origin, specific working principle, and development history of GAN, various applications of GAN in digital image processing, Cycle-GAN, and its application in medical imaging analysis, as well as the latest applications of GAN in medical informatics and bioinformatics.

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Section: Application Of Gan In Image Processingmentioning

confidence: 99%

Generative Adversarial Networks and Its Applications in Biomedical Informatics

Lan

You

Zhang

et al. 2020

Front. Public Health

Self Cite

169

104

View full text Add to dashboard Cite

show abstract

“…Our future work will focus on the application of the data augmentation approach on some more complex problems, e.g. signal processing, image recognition, and image-based fault detection (Chen et al, 2018;Huang et al, 2020;Song et al, 2019;Wang & Yang, 2019;Xu et al, 2018;Zeng et al, 2017), etc.…”

Section: Resultsmentioning

confidence: 99%

Human face recognition based on convolutional neural network and augmented dataset

Song

2020

Systems Science & Control Engineering

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To deal with the issue of human face recognition on small original dataset, a new approach combining convolutional neural network (CNN) with augmented dataset is developed in this paper. The original small dataset is augmented to be a large dataset via several transformations of the face images. Based on the augmented face image dataset, the feature of the faces can be effectively extracted and higher face recognition accuracy can be achieved by using the ingenious CNN. The effectiveness and superiority of the proposed approach can be verified by several experiments and comparisons with some frequently used face recognition methods.

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“…Figure 2 gives a toy example that consists of two conv layers, two pooling layers, and two fully connected layers. CNN can achieve comparable or even better performance than traditional AI approaches, while it does not need to manual design the features (Zeng et al, 2014 , 2016a , b , 2017a , b ).…”

Section: Methodsmentioning

confidence: 99%

Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling

et al. 2018

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Aim: Multiple sclerosis is a severe brain and/or spinal cord disease. It may lead to a wide range of symptoms. Hence, the early diagnosis and treatment is quite important.Method: This study proposed a 14-layer convolutional neural network, combined with three advanced techniques: batch normalization, dropout, and stochastic pooling. The output of the stochastic pooling was obtained via sampling from a multinomial distribution formed from the activations of each pooling region. In addition, we used data augmentation method to enhance the training set. In total 10 runs were implemented with the hold-out randomly set for each run.Results: The results showed that our 14-layer CNN secured a sensitivity of 98.77 ± 0.35%, a specificity of 98.76 ± 0.58%, and an accuracy of 98.77 ± 0.39%.Conclusion: Our results were compared with CNN using maximum pooling and average pooling. The comparison shows stochastic pooling gives better performance than other two pooling methods. Furthermore, we compared our proposed method with six state-of-the-art approaches, including five traditional artificial intelligence methods and one deep learning method. The comparison shows our method is superior to all other six state-of-the-art approaches.

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Denoising and deblurring gold immunochromatographic strip images via gradient projection algorithms

Cited by 60 publications

References 23 publications

Generative Adversarial Networks and Its Applications in Biomedical Informatics

Generative Adversarial Networks and Its Applications in Biomedical Informatics

Human face recognition based on convolutional neural network and augmented dataset

Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling

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