A proposal based on color descriptors and local binary patterns histogram as support tool in presumptive diagnosis of hiatus hernia

Serpa-Andrade, Luis; Robles-Bykbaev, Vladimir; Calle-Ortiz, Eduardo; Gonzalez-Delgado, Luis; Guevara-Segarra, Gabriela

doi:10.1109/ropec.2014.7036342

2014 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) 2014

DOI: 10.1109/ropec.2014.7036342

|View full text |Cite

A proposal based on color descriptors and local binary patterns histogram as support tool in presumptive diagnosis of hiatus hernia

Luis Serpa-Andrade

Vladimir Robles-Bykbaev

Eduardo Calle-Ortiz

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2017

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Efficient artificial intelligence-based assessment of the gastroesophageal valve with Hill classification through active learning

Kafetzis,

Fuchs,

Sodmann

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Standardized assessment of the gastroesophageal valve during endoscopy, attainable via the Hill classification, is important for clinical assessment and therapeutic decision making. The Hill classification is associated with the presence of hiatal hernia (HH), a common endoscopic finding connected to gastro-esophageal reflux disease. A novel efficient medical artificial intelligence (AI) training pipeline using active learning (AL) is designed. We identified 21,970 gastroscopic images as training data and used our AL to train a model for predicting the Hill classification and detecting HH. Performance of the AL and traditionally trained models were evaluated on an external expert-annotated image collection. The AL model achieved accuracy of 76%. A traditionally trained model with 125% more training data achieved 77% accuracy. Furthermore, the AL model achieved higher precision than the traditional one for rare classes, with 0.54 versus 0.39 (p < 0.05) for grade 3 and 0.72 versus 0.61 (p < 0.05) for grade 4. In detecting HH, the AL model achieved 94% accuracy, 0.72 precision and 0.74 recall. Our AL pipeline is more efficient than traditional methods in training AI for endoscopy.

show abstract

Efficient artificial intelligence-based assessment of the gastroesophageal valve with Hill classification through active learning

Kafetzis,

Fuchs,

Sodmann

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

Improved opponent color local binary patterns: an effective local image descriptor for color texture classification

Bianconi

Bello-Cerezo

Napoletano

2017

J. Electron. Imaging

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A proposal based on color descriptors and local binary patterns histogram as support tool in presumptive diagnosis of hiatus hernia

Cited by 2 publications

References 12 publications

Efficient artificial intelligence-based assessment of the gastroesophageal valve with Hill classification through active learning

Efficient artificial intelligence-based assessment of the gastroesophageal valve with Hill classification through active learning

Improved opponent color local binary patterns: an effective local image descriptor for color texture classification

Contact Info

Product

Resources

About