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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Bibliographic Details
Main Authors:	Esra Sivari, Erkan Bostanci, Mehmet Serdar Guzel, Koray Acici, Tunc Asuroglu, Tulin Ercelebi Ayyildiz
Format:	Artigo
Language:	Inglês
Published:	MDPI AG 2023-02-01
Series:	Diagnostics
Subjects:	deep learning stacking ensemble learning gastrointestinal tract classification endoscopy images McNemar’s test
Online Access:	https://www.mdpi.com/2075-4418/13/4/720
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A New Approach for Gastrointestinal Tract Findings Detection and Classification: Deep Learning-Based Hybrid Stacking Ensemble Models

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