ArticleJournal of imaging2021
Comparison of Different Image Data Augmentation Approaches.
Article in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Intelligent Corrosion Sensing and Detection for Aerospace Ground Equipment: A YOLOv11-Based Framework with Shallow Attention and Bidirectional Feature Fusion.Sensors (Basel, Switzerland) · 2026Article
- Enhancing pumping unit diagnosis with similarity splicing data augmentation and wavelet denoising.Scientific reports · 2025Article
- Detection and classification of femoral neck fractures from plain pelvic X-rays using deep learning and machine learning methods.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2025Article
- A Machine Learning Approach to Quantitative Analysis of Enamel Microstructure from Scanning Electron Microscopy Images.Small structures · 2025Article
- Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation.Diagnostics (Basel, Switzerland) · 2025Review
- Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers' pneumoconiosis.Biomedical engineering online · 2025Article
- Convolutional Neural Network to Classify Infrared Thermal Images of Fractured Wrists in Pediatrics.Healthcare (Basel, Switzerland) · 2024Article
- [A survey on the application of convolutional neural networks in the diagnosis of occupational pneumoconiosis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images.Medical & biological engineering & computing · 2024Article
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- Efficient dataset extension using generative networks for assessing degree of coating degradation around scribe.Frontiers in artificial intelligence · 2024Article
- DALib: A Curated Repository of Libraries for Data Augmentation in Computer Vision.Journal of imaging · 2023Review
- Design and Validation of a Deep Learning Model for Renal Stone Detection and Segmentation on Kidney-Ureter-Bladder Images.Bioengineering (Basel, Switzerland) · 2023Article
- Recognizing schizophrenia using facial expressions based on convolutional neural network.Brain and behavior · 2023Article
- Data Augmentation in Classification and Segmentation: A Survey and New Strategies.Journal of imaging · 2023Article
- Deep Learning Model for Computer-Aided Diagnosis of Urolithiasis Detection from Kidney-Ureter-Bladder Images.Bioengineering (Basel, Switzerland) · 2022Article
- Compact Hybrid Multi-Color Space Descriptor Using Clustering-Based Feature Selection for Texture Classification.Journal of imaging · 2022Article
- Use data augmentation for a deep learning classification model with chest X-ray clinical imaging featuring coal workers' pneumoconiosis.BMC pulmonary medicine · 2022Observational
- Automated recognition of pain in cats.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Convolutional neural networks (CNNs) have gained prominence in the research literature on image classification over the last decade. One shortcoming of CNNs, however, is their lack of generalizability and tendency to overfit when presented with small training sets. Augmentation directly confronts this problem by generating new data points providing additional information. In this paper, we investigate the performance of more than ten different sets of data augmentation methods, with two novel approaches proposed here: one based on the discrete wavelet transform and the other on the constant-Q Gabor transform. Pretrained ResNet50 networks are finetuned on each augmentation method. Combinations of these networks are evaluated and compared across four benchmark data sets of images representing diverse problems and collected by instruments that capture information at different scales: a virus data set, a bark data set, a portrait dataset, and a LIGO glitches data set. Experiments demonstrate the superiority of this approach. The best ensemble proposed in this work achieves state-of-the-art (or comparable) performance across all four data sets. This result shows that varying data augmentation is a feasible way for building an ensemble of classifiers for image classification.
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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.