A comprehensive survey of recent trends in deep learning for digital images augmentation

Khalifa, Nour Eldeen M.; Loey, Mohamed; Mirjalili, Seyedali

doi:10.1007/s10462-021-10066-4

Cited by 155 publications

(75 citation statements)

References 56 publications

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“…At the peak of this pandemic, a lot of research work [ 5 , 6 ] has been done as well as in progress to help ease in detecting and treating COVID-19 [ 7 , 8 ]. With an inclination towards using deep learning models [ 9 , 10 ] for detection of corona virus, various papers have proposed and showed how to use CNNs and transfer learning to use VGG16, VGG19, ResNet, DenseNet, and other models for fine-tuning and feature extraction in currently available X-ray and CT scan dataset.…”

Section: Related Workmentioning

confidence: 99%

An Improved COVID-19 Detection using GAN-Based Data Augmentation and Novel QuNet-Based Classification

Asghar

Arif

Ejaz

et al. 2022

BioMed Research International

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COVID-19 is a fatal disease caused by the SARS-CoV-2 virus that has caused around 5.3 Million deaths globally as of December 2021. The detection of this disease is a time taking process that have worsen the situation around the globe, and the disease has been identified as a world pandemic by the WHO. Deep learning-based approaches are being widely used to diagnose the COVID-19 cases, but the limitation of immensity in the publicly available dataset causes the problem of model over-fitting. Modern artificial intelligence-based techniques can be used to increase the dataset to avoid from the over-fitting problem. This research work presents the use of various deep learning models along with the state-of-the-art augmentation methods, namely, classical and generative adversarial network- (GAN-) based data augmentation. Furthermore, four existing deep convolutional networks, namely, DenseNet-121, InceptionV3, Xception, and ResNet101 have been used for the detection of the virus in X-ray images after training on augmented dataset. Additionally, we have also proposed a novel convolutional neural network (QuNet) to improve the COVID-19 detection. The comparative analysis of achieved results reflects that both QuNet and Xception achieved high accuracy with classical augmented dataset, whereas QuNet has also outperformed and delivered 90% detection accuracy with GAN-based augmented dataset.

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Section: Related Workmentioning

confidence: 99%

An Improved COVID-19 Detection using GAN-Based Data Augmentation and Novel QuNet-Based Classification

Asghar

Arif

Ejaz

et al. 2022

BioMed Research International

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“…The classic or basic methods are flipping, rotation, shearing, cropping, translation, color space shifting, image filters, noise, and random erasing. Deep learning data augmentation techniques are Generative Adversarial Networks (GANs), Neural Style Transfer, and Meta Metric Learning [23]. Moreover, 3D Computer-aided design (CAD) software and renders are useful for generating synthetic images to train algorithms to perform object recognition [24].…”

Section: Synthetic and Real Datamentioning

confidence: 99%

Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data

et al. 2021

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Electric companies face flow control and inventory obstacles such as reliability, outlays, and time-consuming tasks. Convolutional Neural Networks (CNNs) combined with computational vision approaches can process image classification in warehouse management applications to tackle this problem. This study uses synthetic and real images applied to CNNs to deal with classification of inventory items. The results are compared to seek the neural networks that better suit this application. The methodology consists of fine-tuning several CNNs on Red–Green–Blue (RBG) and Red–Green–Blue-Depth (RGB-D) synthetic and real datasets, using the best architecture of each domain in a blended ensemble approach. The proposed blended ensemble approach was not yet explored in such an application, using RGB and RGB-D data, from synthetic and real domains. The use of a synthetic dataset improved accuracy, precision, recall and f1-score in comparison with models trained only on the real domain. Moreover, the use of a blend of DenseNet and Resnet pipelines for colored and depth images proved to outperform accuracy, precision and f1-score performance indicators over single CNNs, achieving an accuracy measurement of 95.23%. The classification task is a real logistics engineering problem handled by computer vision and artificial intelligence, making full use of RGB and RGB-D images of synthetic and real domains, applied in an approach of blended CNN pipelines.

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“…Reviews on image data augmentation for DL models have already been published [19][20][21][22][23]. In their paper, Shorten et al [19] realised a complete survey on image data augmentation for DL, covering both basic image manipulation and DL approaches.…”

Section: Introductionmentioning

confidence: 99%

“…In their review, the authors focus also on more recent DL approaches: feature space augmentation, adversarial training, GAN-based and Neural Style Transfer. More recently, Khalifa et al [20] grouped the papers of their review on image data augmentation in a similar fashion. In addition, the authors present an analysis of the state of the art specific to different application domains.…”

Section: Introductionmentioning

confidence: 99%

Survey on Videos Data Augmentation for Deep Learning Models

Cauli

Recupero

2022

Future Internet

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In most Computer Vision applications, Deep Learning models achieve state-of-the-art performances. One drawback of Deep Learning is the large amount of data needed to train the models. Unfortunately, in many applications, data are difficult or expensive to collect. Data augmentation can alleviate the problem, generating new data from a smaller initial dataset. Geometric and color space image augmentation methods can increase accuracy of Deep Learning models but are often not enough. More advanced solutions are Domain Randomization methods or the use of simulation to artificially generate the missing data. Data augmentation algorithms are usually specifically designed for single images. Most recently, Deep Learning models have been applied to the analysis of video sequences. The aim of this paper is to perform an exhaustive study of the novel techniques of video data augmentation for Deep Learning models and to point out the future directions of the research on this topic.

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A comprehensive survey of recent trends in deep learning for digital images augmentation

Cited by 155 publications

References 56 publications

An Improved COVID-19 Detection using GAN-Based Data Augmentation and Novel QuNet-Based Classification

An Improved COVID-19 Detection using GAN-Based Data Augmentation and Novel QuNet-Based Classification

Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data

Survey on Videos Data Augmentation for Deep Learning Models

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