Classification on Digital Pathological Images of Breast Cancer Based on Deep Features of Different Levels

Table 14

Extracted features from different feature extraction nodes on image-level F1 score (/%).

Point location	Classifier	Magnifications
		40×	100×	200×	400×
1	SVM	86.33 ± 2.05	87.17 ± 2.28	87.76 ± 2.05	84.28 ± 1.92
	LR	82.80 ± 2.41	85.21 ± 5.32	84.93 ± 7.62	83.67 ± 4.42
	GNB	74.81 ± 3.25	80.91 ± 2.67	82.54 ± 4.03	73.57 ± 4.86
	DT	85.04 ± 1.42	85.69 ± 2.43	88.20 ± 1.27	86.08 ± 3.13
	RF	83.69 ± 3.59	86.19 ± 2.16	88.52 ± 0.89	82.71 ± 6.64
	MFNN	84.07 ± 2.74	85.15 ± 5.0	85.91 ± 4.43	84.62 ± 4.00
2	SVM	91.12 ± 3.77	93.30 ± 4.71	92.54 ± 2.77	90.45 ± 3.41
	LR	87.97 ± 2.10	88.00 ± 2.93	88.73 ± 1.85	86.21 ± 1.51
	GNB	86.61 ± 3.68	86.86 ± 2.95	89.62 ± 2.42	86.05 ± 5.27
	DT	85.84 ± 2.71	86.74 ± 1.60	89.45 ± 1.86	86.23 ± 2.08
	RF	84.21 ± 2.87	87.45 ± 2.00	90.76 ± 1.35	88.23 ± 5.47
	MFNN	86.58 ± 2.40	87.33 ± 3.15	90.19 ± 2.41	87.67 ± 2.74
3	SVM	89.21 ± 0.95	90.98 ± 1.50	92.79 ± 2.66	89.83 ± 3.80
	LR	87.34 ± 1.27	87.55 ± 1.10	88.69 ± 2.54	86.56 ± 3.85
	GNB	85.12 ± 2.58	87.68 ± 1.61	89.55 ± 2.45	86.01 ± 5.00
	DT	85.08 ± 1.71	86.27 ± 2.12	89.62 ± 3.91	86.23 ± 3.40
	RF	84.80 ± 3.06	87.09 ± 1.48	90.04 ± 1.64	86.67 ± 3.21
	MFNN	87.71 ± 1.40	88.98 ± 0.68	90.80 ± 2.75	85.91 ± 2.40