A New Approach for Gastrointestinal Tract Findings Detection and Classification: Deep Learning-Based Hybrid Stacking Ensemble Models

Endoscopic procedures for diagnosing gastrointestinal tract findings depend on specialist experience and inter-observer variability. This variability can cause minor lesions to be missed and prevent early diagnosis. In this study, deep learning-based hybrid stacking ensemble modeling has been propos...

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Detalles Bibliográficos
Autores principales: Sivari, Esra, Bostanci, Erkan, Guzel, Mehmet Serdar, Acici, Koray, Asuroglu, Tunc, Ercelebi Ayyildiz, Tulin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9954881/
https://www.ncbi.nlm.nih.gov/pubmed/36832205
http://dx.doi.org/10.3390/diagnostics13040720

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9954881/
https://www.ncbi.nlm.nih.gov/pubmed/36832205
http://dx.doi.org/10.3390/diagnostics13040720

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