Methodology to develop machine learning algorithms to improve performance in gastrointestinal endoscopy

Table 1 Some existing image datasets for gastrointestinal endoscopy

Dataset	Findings	Frames	Usage
CVC-356[39]	Polyps	1706	©, by request
CVC-612[40]	Polyps	1962	©, by request
CVC-12k[41]	Polyps	11954	©, by request
Kvasir[38]	Polyps, esophagitis, ulcerative colitis, Z-line, pylorus, cecum, dyed polyp, dyed resection margins, stool	8000	Open academic
Nerthus[41]	Stool - categorization of bowel cleanliness	1350	Open academic
GIANA’17[42]	Angiectasia	600	©, by request
ASU-Mayo polyp database[43]	Polyps	18781	©, by request
CVC-ClinicDB	Polyps	612	©, by request
ETIS-Larib Polyp DB	Polyps	1500	©, by request
KID[44]	Angiectasia, bleeding, inflammations, polyps	2500 + 47 videos	Open academic