DGCNN: deep convolutional generative adversarial network based convolutional neural network for diagnosis of COVID-19

Laddha, Saloni; Kumar, Vijay

doi:10.1007/s11042-022-12640-6

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…The primary objective of GAN is to render the discriminator network incapable of differentiating between the output data generated by the generator network and real data ( Figure 2C ). More recently, deep convolutional GANs (DCGANs) have emerged by combining CNNs and GANs to achieve better performance and effectiveness, resulting in their increasing popularity for designing various computer-aided diagnosis (CADx) models ( 53 , 74 – 76 ).…”

Section: Overview Of Deep Learning Techniquesmentioning

confidence: 99%

Research progress on deep learning in magnetic resonance imaging–based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

He,

Cao,

Chi

et al. 2023

Front. Oncol.

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Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.

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Section: Overview Of Deep Learning Techniquesmentioning

confidence: 99%

Research progress on deep learning in magnetic resonance imaging–based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

He,

Cao,

Chi

et al. 2023

Front. Oncol.

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Medical image identification methods: A review

Li,

Jiang,

et al. 2024

Computers in Biology and Medicine

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A Survey on GAN Techniques for Data Augmentation to Address the Imbalanced Data Issues in Credit Card Fraud Detection

Strelcenia

Prakoonwit

2023

MAKE

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Data augmentation is an important procedure in deep learning. GAN-based data augmentation can be utilized in many domains. For instance, in the credit card fraud domain, the imbalanced dataset problem is a major one as the number of credit card fraud cases is in the minority compared to legal payments. On the other hand, generative techniques are considered effective ways to rebalance the imbalanced class issue, as these techniques balance both minority and majority classes before the training. In a more recent period, Generative Adversarial Networks (GANs) are considered one of the most popular data generative techniques as they are used in big data settings. This research aims to present a survey on data augmentation using various GAN variants in the credit card fraud detection domain. In this survey, we offer a comprehensive summary of several peer-reviewed research papers on GAN synthetic generation techniques for fraud detection in the financial sector. In addition, this survey includes various solutions proposed by different researchers to balance imbalanced classes. In the end, this work concludes by pointing out the limitations of the most recent research articles and future research issues, and proposes solutions to address these problems.

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DGCNN: deep convolutional generative adversarial network based convolutional neural network for diagnosis of COVID-19

Cited by 3 publications

References 19 publications

Research progress on deep learning in magnetic resonance imaging–based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

Research progress on deep learning in magnetic resonance imaging–based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

Medical image identification methods: A review

A Survey on GAN Techniques for Data Augmentation to Address the Imbalanced Data Issues in Credit Card Fraud Detection

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