ArticleJournal of imaging2021
Comparing Stacking Ensemble Techniques to Improve Musculoskeletal Fracture Image Classification.
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 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Optimizing PGRs for in vitro shoot proliferation of pomegranate with bayesian-tuned ensemble stacking regression and NSGA-II: a comparative evaluation of machine learning models.Plant methods · 2024Article
- Enhancing genomic prediction with Stacking Ensemble Learning in Arabica Coffee.Frontiers in plant science · 2024Article
- Trustworthy deep learning framework for the detection of abnormalities in X-ray shoulder images.PloS one · 2024Article
- Non-annotated renal histopathological image analysis with deep ensemble learning.Quantitative imaging in medicine and surgery · 2023Article
- Deep Transfer Learning with Enhanced Feature Fusion for Detection of Abnormalities in X-ray Images.Cancers · 2023Article
- Brain Tumor Classification based on Improved Stacked Ensemble Deep Learning Methods.Asian Pacific journal of cancer prevention : APJCP · 2023Article
- Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting.Frontiers in artificial intelligence · 2023Article
- Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram: Using Samples Representing South Korea.Frontiers in psychiatry · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Bone fractures are among the main reasons for emergency room admittance and require a rapid response from doctors. Bone fractures can be severe and can lead to permanent disability if not treated correctly and rapidly. Using X-ray imaging in the emergency room to detect fractures is a challenging task that requires an experienced radiologist, a specialist who is not always available. The availability of an automatic tool for image classification can provide a second opinion for doctors operating in the emergency room and reduce the error rate in diagnosis. This study aims to increase the existing state-of-the-art convolutional neural networks' performance by using various ensemble techniques. In this approach, different CNNs (Convolutional Neural Networks) are used to classify the images; rather than choosing the best one, a stacking ensemble provides a more reliable and robust classifier. The ensemble model outperforms the results of individual CNNs by an average of 10%.
Indexed as
Identifiers
What OpenQuestion holds
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.