A Novel DenseNet Generative Adversarial Network for Heterogenous Low-Light Image Enhancement

Zhang, Jingsi; Wu, Chengdong; Yu, Xiaoqing; Lei, Xiaoliang

doi:10.3389/fnbot.2021.700011

Cited by 6 publications

(8 citation statements)

References 29 publications

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“…GITHUB portal ( accessed on 7 December 2021) is utilized to implement the existing CNN architecture. The studies [ 10 , 18 , 21 ] are employed to evaluate the performance of the proposed CNN (PCNN) model. In addition, CNN models, including GoogleNet and Inception V3, are used for performance evaluation.…”

Section: Methodsmentioning

confidence: 99%

“…The study [ 10 ] developed and implemented a heterogeneous low-light image-enhancing approach based on DenseNet generative adversarial network. Initially, a generative adversarial network is implemented using the DenseNet framework.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Traditional methods of investigating cardiac disease are complex [ 7 , 8 , 9 , 10 ]. The lack of medical diagnostic instruments and automated systems makes pulmonary heart disease detection and treatment challenging in developing nations.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Coronary Artery Disease from Computed Tomography Images Using a Deep Learning Technique

2022

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In recent times, coronary artery disease (CAD) has become one of the leading causes of morbidity and mortality across the globe. Diagnosing the presence and severity of CAD in individuals is essential for choosing the best course of treatment. Presently, computed tomography (CT) provides high spatial resolution images of the heart and coronary arteries in a short period. On the other hand, there are many challenges in analyzing cardiac CT scans for signs of CAD. Research studies apply machine learning (ML) for high accuracy and consistent performance to overcome the limitations. It allows excellent visualization of the coronary arteries with high spatial resolution. Convolutional neural networks (CNN) are widely applied in medical image processing to identify diseases. However, there is a demand for efficient feature extraction to enhance the performance of ML techniques. The feature extraction process is one of the factors in improving ML techniques’ efficiency. Thus, the study intends to develop a method to detect CAD from CT angiography images. It proposes a feature extraction method and a CNN model for detecting the CAD in minimum time with optimal accuracy. Two datasets are utilized to evaluate the performance of the proposed model. The present work is unique in applying a feature extraction model with CNN for CAD detection. The experimental analysis shows that the proposed method achieves 99.2% and 98.73% prediction accuracy, with F1 scores of 98.95 and 98.82 for benchmark datasets. In addition, the outcome suggests that the proposed CNN model achieves the area under the receiver operating characteristic and precision-recall curve of 0.92 and 0.96, 0.91 and 0.90 for datasets 1 and 2, respectively. The findings highlight that the performance of the proposed feature extraction and CNN model is superior to the existing models.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Detecting Coronary Artery Disease from Computed Tomography Images Using a Deep Learning Technique

2022

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“…The Generative Adversarial Network (GAN) obeys the two-person zero-sum game theory [27,28]. The sum of the interests of the two game participants is a constant, and one of the two players is the generative model (G) and the other is the discriminative model D), and the two parties have different functions.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

An Interactive Self-Learning Game and Evolutionary Approach Based on Non-Cooperative Equilibrium

Zhao

Zhang

et al. 2021

Electronics

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Most current studies on multi-agent evolution based on deep learning take a cooperative equilibrium strategy, while interactive self-learning is not always considered. An interactive self-learning game and evolution method based on non-cooperative equilibrium (ISGE-NCE) is proposed to take the benefits of both game theory and interactive learning for multi-agent confrontation evolution. A generative adversarial network (GAN) is designed combining with multi-agent interactive self-learning, and the non-cooperative equilibrium strategy is well adopted within the framework of interactive self-learning, aiming for high evolution efficiency and interest. For assessment, three typical multi-agent confrontation experiments are designed and conducted. The results show that, first, in terms of training speed, the ISGE-NCE produces a training convergence rate of at least 46.3% higher than that of the method without considering interactive self-learning. Second, the evolution rate of the interference and detection agents reaches 60% and 80%, respectively, after training by using our method. In the three different experiment scenarios, compared with the DDPG, our ISGE-NCE method improves the multi-agent evolution effectiveness by 43.4%, 50%, and 20%, respectively, with low training costs. The performances demonstrate the significant superiority of our ISGE-NCE method in swarm intelligence.

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“…Algorithm. e DenseNet algorithm is a neural network that uses feature combinations and set bypasses to improve the performance of the network, which enables the summation of the features of the two pathways before and after the block by means of dense connections [9], which in turn improves the reuse of the features by the network. One L-layer DenseNet network includes L(L + 1)/2 connections to ensure that model short-circuit values occur in blocks.…”

Section: Introduction To Densenetmentioning

confidence: 99%

Study on the Grading Model of Hepatic Steatosis Based on Improved DenseNet

Yang

Zhou

Liu

et al. 2022

Journal of Healthcare Engineering

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To achieve intelligent grading of hepatic steatosis, a deep learning-based method for grading hepatic steatosis was proposed by introducing migration learning in the DenseNet model, and the effectiveness of the method was verified by applying it to the practice of grading hepatic steatosis. The results show that the proposed method can significantly reduce the number of model iterations and improve the model convergence speed and prediction accuracy by introducing migration learning in the deep learning DenseNet model, with an accuracy of more than 85%, sensitivity of more than 94%, specificity of about 80%, and good prediction performance on the training and test sets. It can also detect hepatic steatosis grade 1 more accurately and reliably, and achieve automated and more accurate grading, which has some practical application value.

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A Novel DenseNet Generative Adversarial Network for Heterogenous Low-Light Image Enhancement

Cited by 6 publications

References 29 publications

Detecting Coronary Artery Disease from Computed Tomography Images Using a Deep Learning Technique

Detecting Coronary Artery Disease from Computed Tomography Images Using a Deep Learning Technique

An Interactive Self-Learning Game and Evolutionary Approach Based on Non-Cooperative Equilibrium

Study on the Grading Model of Hepatic Steatosis Based on Improved DenseNet

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