GIT-Net: An Ensemble Deep Learning-Based GI Tract Classification of Endoscopic Images

This paper presents an ensemble of pre-trained models for the accurate classification of endoscopic images associated with Gastrointestinal (GI) diseases and illnesses. In this paper, we propose a weighted average ensemble model called GIT-NET to classify GI-tract diseases. We evaluated the model on...

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Detalles Bibliográficos
Autores principales: Gunasekaran, Hemalatha, Ramalakshmi, Krishnamoorthi, Swaminathan, Deepa Kanmani, J, Andrew, Mazzara, Manuel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10376874/
https://www.ncbi.nlm.nih.gov/pubmed/37508836
http://dx.doi.org/10.3390/bioengineering10070809

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10376874/
https://www.ncbi.nlm.nih.gov/pubmed/37508836
http://dx.doi.org/10.3390/bioengineering10070809

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