DLA-E: a deep learning accelerator for endoscopic images classification

Journal of Big Data

Table 2 A summary of obtained results related to endoscopic image dataset classification

No	Class / DNN Model Precision	Inception V3	ResNet V2	Inception_ResNet V2	VGG19	MobileNet V3
1	Pylorus	0.91	0.92	0.93	0.91	0.95
2	Cecum	0.85	0.85	0.86	0.85	0.86
3	Z-line	0.87	0.84	0.93	0.96	0.95
4	Esophagitis	0.93	0.93	0.96	0.96	0.96
5	Polyps	0.97	0.99	0.96	0.88	0.94
6	Ulcerative Colitis	0.77	0.78	0.82	0.83	0.77
7	Dyed and Lifted Polyps	0.94	0.99	0.96	0.91	0.93
8	Dyed Resection Margins	0.97	0.96	0.98	0.96	0.97
Average Accuracy		0.90	0.91	0.93	0.91	0.92

Bold values indicate better results (Avarage Accuracy) in comparison to other results