An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features

Table 1

Comparison among different polyp detection.

Paper	Used methodology	Used dataset	Result
			Accuracy	Sensitivity	Specificity
Kodogiannis et al. [3]	Texture + ANFIS	140 images		97%
Park et al. [13]	CNN + CRF	35 videos		86%	85%
Ribeiro et al. [9]	CNN	100 images	90.96%	95.16%	74.19%
Zhu et al. [7]	CNN + SVM	180 images	80%	79%	79.54%
Alexandre et al. [4]	RGB + XY + SVM	4620 images	94.87%
Zou et al. [6]	DCNN	25 videos	95%
Li et al. [5]	Color + shape + MLP	450 images	94.20%	95.07%	93.33%
Karkanis et al. [1]	CWC + LDA	60 videos		97%	90%
Iakovidis et al. [2]	KL + wavelet + SVM	86 videos	94%
Proposed system	Color wavelet + CNN + SVM	100 videos	98.65%	98.79%	98.52%