Generative Adversarial Networks and Its Applications in Biomedical Informatics

Frontiers in Public Health, Journal Year: 2020, Volume and Issue: 8

Published: May 12, 2020

The basic Generative Adversarial Networks (GAN) model is composed of the input vector, generator, and discriminator. Among them, generator discriminator are implicit function expressions, usually implemented by deep neural networks. GAN can learn generative 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 origin, specific working principle, development history GAN, various applications digital image processing, Cycle-GAN its application medical imaging analysis, as well latest informatics bioinformatics.

Language: Английский

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Deep Learning Algorithms for Cybersecurity Applications: A Technological and Status Review

Computer Science Review, Journal Year: 2020, Volume and Issue: 39, P. 100317 - 100317

Published: Dec. 1, 2020

Language: Английский

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Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Plants, Journal Year: 2020, Volume and Issue: 9(10), P. 1302 - 1302

Published: Oct. 1, 2020

Deep learning (DL) represents the golden era in machine (ML) domain, and it has gradually become leading approach many fields. It is currently playing a vital role early detection classification of plant diseases. The use ML techniques this field viewed as having brought considerable improvement cultivation productivity sectors, particularly with recent emergence DL, which seems to have increased accuracy levels. Recently, DL architectures been implemented accompanying visualisation that are essential for determining symptoms classifying This review investigates analyses most methods, developed over three years up 2020, training, augmentation, feature fusion extraction, recognising counting crops, detecting diseases, including how these methods can be harnessed feed deep classifiers their effects on classifier accuracy.

Language: Английский

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MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer’s Disease: A Survey

Sensors, Journal Year: 2020, Volume and Issue: 20(11), P. 3243 - 3243

Published: June 7, 2020

Many neurological diseases and delineating pathological regions have been analyzed, the anatomical structure of brain researched with aid magnetic resonance imaging (MRI). It is important to identify patients Alzheimer’s disease (AD) early so that preventative measures can be taken. A detailed analysis tissue structures from segmented MRI leads a more accurate classification specific disorders. Several segmentation methods diagnose AD proposed varying complexity. Segmentation using deep learning approaches has gained attention as it provide effective results over large set data. Hence, are now preferred state-of-the-art machine methods. We aim an outline current learning-based for quantitative diagnosis AD. Here, we report how convolutional neural network architectures used analyze AD, discuss improves classification, describe approaches, summarize their publicly available datasets. Finally, insight into issues possible future research directions in building computer-aided diagnostic system

Language: Английский

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Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models

Agricultural Water Management, Journal Year: 2020, Volume and Issue: 245, P. 106547 - 106547

Published: Oct. 8, 2020

Language: Английский

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