ArticleArtificial intelligence review2022
A comprehensive survey of recent trends in deep learning for digital images augmentation.
Article in Artificial intelligence review, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 3 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Accuracy of artificial intelligence in detecting tumor bone metastases: a systematic review and meta-analysis.BMC cancer · 2025Pooled it
- The accuracy of artificial intelligence used for non-melanoma skin cancer diagnoses: a meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Towards Home-Based Diabetic Foot Ulcer Monitoring: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Cross-Attention-Driven Propose-and-Select Generative Data Augmentation for Few-Shot Image Classification.Sensors (Basel, Switzerland) · 2026Article
- Classification of spinal tuberculous infection, pyogenic infection and spinal metastasis from magnetic resonance imaging using machine learning.BMC musculoskeletal disorders · 2026Article
- Boosting foundation models for rare eye disease diagnosis via a multimodal text-to-image generative framework.NPJ digital medicine · 2026Article
- A Systematic Review of Imaging Techniques for the Botanical and Geographical Classification of Coffee.Foods (Basel, Switzerland) · 2026Review
- WISeRKNet: wide slice residual Kronecker network for lung cancer detection based on CT images.Scientific reports · 2026Article
- Beyond classification metrics: a psychometric-aware benchmark for data augmentation in imbalanced student mental health surveys.Frontiers in digital health · 2026Article
- High-resolution conditional MR image synthesis through the PACGAN framework.Scientific reports · 2025Article
- Leveraging Deep Learning to Address Diagnostic Challenges with Insufficient Image Data.ACS sensors · 2025Article
- BCDCNN: breast cancer deep convolutional neural network for breast cancer detection using MRI images.Scientific reports · 2025Article
- A Computer Vision Method for Finding Mislabelled Specimens Within Natural History Collections.Ecology and evolution · 2025Article
- Accurate and visualiable discrimination of Chenpi age using 2D-CNN and Grad-CAM++ based on infrared spectral images.Food chemistry: X · 2024Article
- A robust deep learning attack immune MRAM-based physical unclonable function.Scientific reports · 2024Article
- DeepDate: A deep fusion model based on whale optimization and artificial neural network for Arabian date classification.PloS one · 2024Article
- ONDL: An optimized Neutrosophic Deep Learning model for classifying waste for sustainability.PloS one · 2024Article
- Contrastive learning with token projection for Omicron pneumonia identification from few-shot chest CT images.Frontiers in medicine · 2024Article
- Constraints on Optimising Encoder-Only Transformers for Modelling Sign Language with Human Pose Estimation Keypoint Data.Journal of imaging · 2023Article
- DALib: A Curated Repository of Libraries for Data Augmentation in Computer Vision.Journal of imaging · 2023Review
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Deep learning proved its efficiency in many fields of computer science such as computer vision, image classifications, object detection, image segmentation, and more. Deep learning models primarily depend on the availability of huge datasets. Without the existence of many images in datasets, different deep learning models will not be able to learn and produce accurate models. Unfortunately, several fields don't have access to large amounts of evidence, such as medical image processing. For example. The world is suffering from the lack of COVID-19 virus datasets, and there is no benchmark dataset from the beginning of 2020. This pandemic was the main motivation of this survey to deliver and discuss the current image data augmentation techniques which can be used to increase the number of images. In this paper, a survey of data augmentation for digital images in deep learning will be presented. The study begins and with the introduction section, which reflects the importance of data augmentation in general. The classical image data augmentation taxonomy and photometric transformation will be presented in the second section. The third section will illustrate the deep learning image data augmentation. Finally, the fourth section will survey the state of the art of using image data augmentation techniques in the different deep learning research and application.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.