ArticleHealth information science and systems2021
Improving convolutional neural networks performance for image classification using test time augmentation: a case study using MURA dataset.
Article in Health information science and systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Automated classification of musculoskeletal abnormalities using a dynamic ensemble of deep vision models.Medical & biological engineering & computing · 2026Article
- Uncertainty Quantification of Central Canal Stenosis Deep Learning Classifier From Lumbar Sagittal T2-Weighted MRI.JOR spine · 2026Article
- A Gated Attention-Based Multiple Instance Learning and Test-Time Augmentation Approach for Diagnosing Active Sacroiliitis in Sacroiliac Joint MRI Scans.Journal of clinical medicine · 2026Article
- Deep-Fed: A comprehensive solution for precise bone fracture identification in athletes.PloS one · 2026Article
- Addressing data heterogeneity in distributed medical imaging with heterosync learning.Nature communications · 2025Article
- Molecular-informed image classification for predicting drug sensitivity in cancer therapy.Frontiers in oncology · 2025Article
- Enhancing mental health diagnostics through deep learning-based image classification.Frontiers in medicine · 2025Article
- The Constrained Disorder Principle Overcomes the Challenges of Methods for Assessing Uncertainty in Biological Systems.Journal of personalized medicine · 2024Review
- Development of a diagnostic support system for distal humerus fracture using artificial intelligence.International orthopaedics · 2024Article
- IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques.Sensors (Basel, Switzerland) · 2023Article
- Data Augmentation in Classification and Segmentation: A Survey and New Strategies.Journal of imaging · 2023Article
- DeepDRiD: Diabetic Retinopathy-Grading and Image Quality Estimation Challenge.Patterns (New York, N.Y.) · 2022Article
- LungNet22: A Fine-Tuned Model for Multiclass Classification and Prediction of Lung Disease Using X-ray Images.Journal of personalized medicine · 2022Article
- Convolutional Neural Network-Based Computer-Assisted Diagnosis of Hashimoto's Thyroiditis on Ultrasound.The Journal of clinical endocrinology and metabolism · 2022Article
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2 authors.
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Abstract
Bone fractures are one of the main causes to visit the emergency room (ER); the primary method to detect bone fractures is using X-Ray images. X-Ray images require an experienced radiologist to classify them; however, an experienced radiologist is not always available in the ER. An accurate automatic X-Ray image classifier in the ER can help reduce error rates by providing an instant second opinion to the emergency doctor. Deep learning is an emerging trend in artificial intelligence, where an automatic classifier can be trained to classify musculoskeletal images. Image augmentations techniques have proven their usefulness in increasing the deep learning model's performance. Usually, in the image classification domain, the augmentation techniques are used during training the network and not during the testing phase. Test time augmentation (TTA) can increase the model prediction by providing, with a negligible computational cost, several transformations for the same image. In this paper, we investigated the effect of TTA on image classification performance on the MURA dataset. Nine different augmentation techniques were evaluated to determine their performance compared to predictions without TTA. Two ensemble techniques were assessed as well, the majority vote and the average vote. Based on our results, TTA increased classification performance significantly, especially for models with a low score.
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