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deep convolutional generative adversarial networks to Enhance Artificial Intelligence in Healthcare: A Skin Cancer Application

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SENSORS 2022年第16期22卷 42-45页

作者： La Salvia, Marco Torti, Emanuele Leon, Raquel Fabelo, Himar Ortega, Samuel Martinez-Vega, Beatriz Callico, Gustavo M. Leporati, Francesco Univ Pavia Dept Elect Comp & Biomed Engn I-27100 Pavia Italy Univ Las Palmas de Gran Canaria ULPGC Res Inst Appl Microelect IUMA Las Palmas Gran Canaria 35001 Spain Norwegian Inst Food Fisheries & Aquaculture Res N N-6122 Tromso Norway

In recent years, researchers designed several artificial intelligence solutions for healthcare applications, which usually evolved into functional solutions for clinical practice. Furthermore, deep learning (DL) methods are well-suited to process the broad amounts of data acquired by wearable devices, smartphones, and other sensors employed in different medical domains. Conceived to serve the role of diagnostic tool and surgical guidance, hyperspectral images emerged as a non-contact, non-ionizing, and label-free technology. However, the lack of large datasets to efficiently train the models limits DL applications in the medical field. Hence, its usage with hyperspectral images is still at an early stage. We propose a deep convolutional generative adversarial network to generate synthetic hyperspectral images of epidermal lesions, targeting skin cancer diagnosis, and overcome small-sized datasets challenges to train DL architectures. Experimental results show the effectiveness of the proposed framework, capable of generating synthetic data to train DL classifiers.

关键词： deep learning hyperspectral imaging medical hyperspectral images synthetic data generation deep convolutional generative adversarial networks

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DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks

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BMC GENOMICS 2024年第1期25卷 411页

作者： Kalemati, Mahmood Zamani Emani, Mojtaba Koohi, Somayyeh Sharif Univ Technol Dept Comp Engn Tehran Iran

Background In recent years, there has been a growing interest in utilizing computational approaches to predict drug-target binding affinity, aiming to expedite the early drug discovery process. To address the limitations of experimental methods, such as cost and time, several machine learning-based techniques have been developed. However, these methods encounter certain challenges, including the limited availability of training data, reliance on human intervention for feature selection and engineering, and a lack of validation approaches for robust evaluation in real-life *** To mitigate these limitations, in this study, we propose a method for drug-target binding affinity prediction based on deep convolutional generative adversarial networks. Additionally, we conducted a series of validation experiments and implemented adversarial control experiments using straw models. These experiments serve to demonstrate the robustness and efficacy of our predictive models. We conducted a comprehensive evaluation of our method by comparing it to baselines and state-of-the-art methods. Two recently updated datasets, namely the BindingDB and PDBBind, were used for this purpose. Our findings indicate that our method outperforms the alternative methods in terms of three performance measures when using warm-start data splitting settings. Moreover, when considering physiochemical-based cold-start data splitting settings, our method demonstrates superior predictive performance, particularly in terms of the concordance *** The results of our study affirm the practical value of our method and its superiority over alternative approaches in predicting drug-target binding affinity across multiple validation sets. This highlights the potential of our approach in accelerating drug repurposing efforts, facilitating novel drug discovery, and ultimately enhancing disease treatment. The data and source code for this study were deposited in the GitHub repository, htt

关键词： Drug-target binding affinity deep convolutional generative adversarial networks BLOSUM encoding adversarial control experiments Straw models

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Automatic reconstruction method of 3D geological models based on deep convolutional generative adversarial networks

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COMPUTATIONAL GEOSCIENCES 2022年第5期26卷 1135-1150页

作者： Yang, Zixiao Chen, Qiyu Cui, Zhesi Liu, Gang Dong, Shaoqun Tian, Yiping China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Peoples R China China Univ Geosci State Key Lab Biogeol & Environm Geol Wuhan 430074 Peoples R China China Univ Petr Coll Sci Beijing 102249 Peoples R China

How to reconstruct a credible three-dimensional (3D) geological model from very limited survey data, e.g. boreholes, outcrop, and two-dimensional (2D) images, is challenging in the field of 3D geological modeling. Against the limitations of the huge computational consumption and complex parameterization of geostatistics-based stochastic simulation methods, we propose an automatic reconstruction method of 3D geological models based on deep convolutional generative adversarial network (DCGAN). In this work, 2D geological sections are used as conditioning data to generate 3D geological models automatically. Various realizations can be reproduced under a same DCGAN model established through deep network training. A U-Net structure is used to enhance the fitting effect of the DCGAN model. In addition, joint loss functions are exploited to increase the similarity between 3D realizations and reference models. Three synthetic datasets were used to verify the capability of the method presented in this paper. Experimental results show that the proposed 3D automatic reconstruction method based on DCGAN can capture the features, trends and spatial patterns of geological structures well. The output models obey the used conditioning data. The complex heterogeneous structures are reconstructed more accurately and quickly by using the proposed method.

关键词： deep convolutional generative adversarial networks Three-dimensional reconstruction Heterogeneous structures Geostatistical simulation deep learning

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A Novel Chaotic Block Image Encryption Algorithm Based on deep convolutional generative adversarial networks

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IEEE ACCESS 2021年 9卷 18497-18517页

作者： Fang, Pengfei Liu, Han Wu, Chengmao Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China Xian Univ Posts & Telecommun Sch Elect Engn Xian 710061 Peoples R China

This paper proposes a novel chaotic block image encryption algorithm based on deep convolutional generative adversarial networks (DCGANs), quaternions, an improved Feistel network, and an overall scrambling and diffusion mechanism. First, a new hyperchaotic system is introduced and combined with DCGANs to generate a random sequence with better randomness and complexity as a key stream. This sequence is then combined with a quaternion and an improved Feistel network encryption of a colour plaintext image by utilizing the key block matrix to ultimately achieve overall scrambling and diffusion of the cipher image. Finally, the security of this algorithm is quantitatively and qualitatively analysed. The simulation results show that the proposed hyperchaotic system has a large key space and good random characteristics and that the new algorithm yields adequate security and can resist brute-force attacks and chosen-plaintext attacks. Therefore, this approach provides a new way to achieve secure transmission and protection of image information.

关键词： Encryption Heuristic algorithms Cryptography Streaming media generative adversarial networks DNA Resists Image encryption chaotic system deep convolutional generative adversarial networks

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GAGAN: Enhancing Image Generation Through Hybrid Optimization of Genetic Algorithms and deep convolutional generative adversarial networks

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ALGORITHMS 2024年第12期17卷 584-584页

作者： Konstantopoulou, Despoina Zacharia, Paraskevi Papoutsidakis, Michail Leligou, Helen C. Patrikakis, Charalampos Univ West Attica Dept Ind Design & Prod Engn Athens 12241 Greece Univ West Attica Dept Elect & Elect Engn Athens 12241 Greece

generative adversarial networks (GANs) are highly effective for generating realistic images, yet their training can be unstable due to challenges such as mode collapse and oscillatory convergence. In this paper, we propose a novel hybrid optimization method that integrates Genetic Algorithms (GAs) to improve the training process of deep convolutional GANs (DCGANs). Specifically, GAs are used to evolve the discriminator's weights, complementing the gradient-based learning typically employed in GANs. The proposed GAGAN model is trained on the CelebA dataset, using 2000 images, to generate 128 x 128 images, with the generator learning to produce realistic faces from random latent vectors. The discriminator, which classifies images as real or fake, is optimized not only through standard backpropagation, but also through a GA framework that evolves its weights via crossover, mutation, and selection processes. This hybrid method aims to enhance convergence stability and boost image quality by balancing local search from gradient-based methods with the global search capabilities of GAs. Experiments show that the proposed approach reduces generator loss and improves image fidelity, demonstrating that evolutionary algorithms can effectively complement deep learning techniques. This work opens new avenues for optimizing GAN training and enhancing performance in generative models.

关键词： image generation generative AI genetic algorithms deep convolutional generative adversarial networks discriminator weight optimization

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A Novel Self-Updating Design Method for Complex 3D Structures Using Combined convolutional Neuron and deep convolutional generative adversarial networks

ADVANCED INTELLIGENT SYSTEMS

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ADVANCED INTELLIGENT SYSTEMS 2022年第6期4卷

作者： Gu, Zewen Hou, Xiaonan Saafi, Mohamed Ye, Jianqiao Univ Lancaster Dept Engn Engn Bldg Lancaster LA1 4YW England

Mechanical design is one of the essential disciplines in engineering applications, while inspirations of design ideas highly depend on the ability and prior knowledge of engineers or designers. With the rapid development of machine learning (ML) techniques, artificial intelligence (AI)-based design methods are promising tools for the design of advanced engineering systems. So far, there have been some studies of 2D patterns and structural designs based on ML techniques. However, a particular challenge remains in allowing complex 3D mechanical designs using ML techniques. Herein, a novel and experience-free method to equip ML models with 3D design capabilities by combining a convolutional neuron network (CNN) with a deep convolutional generative adversarial network (DCGAN) is developed. The model directly receives 2D image-based training data that define the complex 3D structures of a specific machine part. After the training process, an infinite number of new 3D designs can be generated by the proposed model, with their geometric and mechanical properties being accurately predicted at the same time. Moreover, the generated new designs can be fed back to expand the original input datasets for further ML model training and updating.

关键词： convolutional neuron networks deep convolutional generative adversarial networks engineering designs helical structures machine learning

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A threshold self-setting condition monitoring scheme for wind turbine generator bearings based on deep convolutional generative adversarial networks

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MEASUREMENT 2021年 167卷 108234-108234页

作者： Chen, Peng Li, Yu Wang, Kesheng Zuo, Ming J. Heyns, P. Stephan Baggerohr, Stephan Univ Elect Sci & Technol China Sch Mech & Elect Engn Equipment Reliabil Prognost & Hlth Management Lab Chengdu 611731 Peoples R China Univ Alberta Dept Mech Engn Edmonton AB T6G 1H9 Canada Univ Pretoria Ctr Asset Integr Management Dept Mech & Aeronaut Engn Pretoria South Africa

Long-term reliable health condition monitoring (HCM) of a wind turbine is an essential method to avoid catastrophic failure results. Existing unsupervised learning methods, such as auto-encoder (AE) and denoising auto-encoder (DAE) models, are utilized to the condition monitoring of wind turbines. The critical bottleneck of these models for monitoring is to determine a threshold for identifying different health conditions. Unfortunately, the threshold is usually set up with different kinds of calculation methods or even based on experience. Therefore, the uncertainty of the threshold will inevitably influence the accuracy of the monitoring process and may lead to misdiagnoses. To overcome this limitation, this research introduces a threshold self-setting HCM scheme, based on deep convolutional generative adversarial networks (DCGAN) and employed for defining a self-setting threshold to monitor wind turbine generator bearings. A threshold for HCM can be automatically created through the output of the G network in the DCGAN model, and the challenging problem of setting up a threshold can be solved. Besides, the use of Nash Equilibrium for training enables this scheme to become self-defined evaluators with a high level of consistency, without any human intervention and can be treated as a self-defined threshold, and it is a model self-tuning process. Furthermore, a sample discrepancy analysis based on the output of the G network is utilized so that a quantitative indicator of the fault severity in wind turbine generator bearings are provided. By tracking a real wind turbine dataset from the LU NAN wind farm in China, the effectiveness of the proposed scheme is verified. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Wind turbine Condition monitoring deep learning deep convolutional generative adversarial networks

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Evolving Levels for General Games Using deep convolutional generative adversarial networks 11

Evolving Levels for General Games Using Deep Convolutional G...

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11th Computer Science and Electronic Engineering Conference (CEEC)

作者： Irfan, Ayesha Zafar, Adeel Hassan, Shahbaz Riphah Int Univ Islamabad Pakistan

ISBN: (纸本)9781728129525

deep convolutional generative adversarial networks (DCGANs) are a machine learning approach that can learn to mimic any distribution of data. DCGANs consist of a generator and discriminator where generator generates new content and discriminator finds whether the generated content is real or fake. Procedural Content Generation (PCG) for level generation could potentially benefit from such models, particularly for a game where there is an existing level to emulate. In this paper, DCGANs are trained on existing levels generated through a random generator. Three different games (Freeway, Zelda, and Colourescape) are selected from the General Video Game Artificial Intelligence (GVG-AI) framework for level generation. The proposed approach successfully generates various levels which mimic the training levels. Generated levels are also evaluated using agent-based testing to ensure their play-ability.

关键词： deep convolutional generative adversarial networks Procedural Content Generation General Video Game Level Generation Agent-Based Testing

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Distant Pedestrian Detection in the Wild using Single Shot Detector with deep convolutional generative adversarial networks

Distant Pedestrian Detection in the Wild using Single Shot D...

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International Joint Conference on Neural networks (IJCNN)

作者： Dinakaran, Ranjith Easom, Philip Zhang, Li Bouridane, Ahmed Jiang, Richard Edirisinghe, Eran Northumbria Univ Comp & Informat Sci Newcastle Upon Tyne Tyne & Wear England Univ Lancaster Comp & Commun Lancaster England Loughborough Univ Comp Sci Loughborough Leics England

ISBN: (纸本)9781728119854

In this work, we examine the feasibility of applying deep convolutional generative adversarial networks (DCGANs) with Single Shot Detector (SSD) as data-processing technique to handle with the challenge of pedestrian detection in the wild. Specifically, we attempted to use in-fill completion to generate random transformations of images with missing pixels to expand existing labelled datasets. In our work, GAN's been trained intensively on low resolution images, in order to neutralize the challenges of the pedestrian detection in the wild, and considered humans, and few other classes for detection in smart cities. The object detector experiment performed by training GAN model along with SSD provided a substantial improvement in the results. This approach presents a very interesting overview in the current state of art on CAN networks for object detection. We used Canadian Institute for Advanced Research (CIFAR), Caltech, KITTI data set for training and testing the network under different resolutions and the experimental results with comparison been showed between DCGAN cascaded with SSD and SSD itself.

关键词： Single Shot Detector Pedestrian Detection deep convolutional generative adversarial networks Smart Cities Surveillance in the Wild

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SEMI-SUPERVISED LEARNING-BASED LIVE FISH IDENTIFICATION IN AQUACULTURE USING MODIFIED deep convolutional generative adversarial networks

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TRANSACTIONS OF THE ASABE 2018年第2期61卷 699-710页

作者： Zhao, J. Li, Y. H. Zhang, F. D. Zhu, S. M. Liu, Y. Lu, H. D. Ye, Z. Y. Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou Zhejiang Peoples R China Max Planck Inst Ornithol Dept Collect Behav Constance Germany Dalian Ocean Univ Sch Marine Sci & Technol & Environm Dalian Peoples R China Zhejiang Univ Ningbo Inst Technol Ningbo Zhejiang Peoples R China

Aiming at live fish identification in aquaculture, a practical and efficient semi-supervised learning model, based on modified deep convolutional generative adversarial networks (DCGANs), was proposed in this study. Benefiting from the modified DCGANs structure, the presented model can be trained effectively using relatively few labeled training samples. In consideration of the complex poses of fish and the low resolution of sampling images in aquaculture, spatial pyramid pooling and some improved techniques specifically for the presented model were used to make the model more robust. Finally, in tests with two preprocessed and challenging datasets (with 5%, 10%, and 15% labeled training data in the fish recognition ground-truth dataset and 25%, 50%, and 75% labeled training data in the Croatian fish dataset), the feasibility and reliability of the presented model for live fish identification were proved with respective accuracies of 80.52%, 81.66%, and 83.07% for the ground-truth dataset and 65.13%, 78.72%, and 82.95% for the Croatian fish dataset.

关键词： Aquaculture deep convolutional generative adversarial networks Few labeled training samples Live fish identification Semi-supervised learning Spatial pyramid pooling

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