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

Table 15

Extracted features from different feature extraction nodes are input into the image-level BAC evaluation results in the ML model (/%).

Point location	Classifier	Magnifications
		40×	100×	200×	400×
1	SVM	80.07 ± 4.28	80.90 ± 3.45	81.77 ± 3.31	80.24 ± 3.98
	LR	76.80 ± 3.66	79.74 ± 4.90	80.44 ± 4.88	78.75 ± 4.74
	GNB	51.83 ± 8.18	69.25 ± 4.68	82.54 ± 4.03	69.74 ± 5.36
	DT	77.64 ± 3.93	78.07 ± 5.01	82.27 ± 2.84	79.47 ± 3.78
	RF	79.20 ± 3.99	78.91 ± 4.52	82.65 ± 2.23	80.39 ± 3.95
	MFNN	77.64 ± 3.86	78.98 ± 5.46	80.18 ± 5.30	77.52 ± 4.37
2	SVM	86.57 ± 4.06	87.64 ± 3.74	89.42 ± 3.31	85.31 ± 5.10
	LR	82.68 ± 4.60	84.05 ± 3.79	86.82 ± 3.46	79.05 ± 3.46
	GNB	73.84 ± 6.93	82.81 ± 2.31	88.62 ± 2.42	80.74 ± 5.36
	DT	80.87 ± 5.54	79.50 ± 4.10	83.68 ± 3.51	79.63 ± 3.88
	RF	80.40 ± 4.40	80.67 ± 4.80	84.04 ± 2.97	80.81 ± 4.38
	MFNN	80.33 ± 4.56	81.54 ± 5.39	87.16 ± 5.02	80.93 ± 3.75
3	SVM	83.21 ± 1.49	84.74 ± 3.22	87.88 ± 4.76	83.88 ± 5.56
	LR	81.49 ± 1.73	82.80 ± 2.93	85.75 ± 4.96	80.37 ± 5.46
	GNB	76.45 ± 5.73	87.68 ± 1.61	87.54 ± 2.45	79.95 ± 7.65
	DT	79.66 ± 3.51	78.85 ± 3.60	82.76 ± 3.71	79.91 ± 5.17
	RF	79.65 ± 3.97	79.27 ± 3.16	83.29 ± 3.15	80.52 ± 4.96
	MFNN	81.97 ± 2.03	83.29 ± 3.11	86.94 ± 5.15	82.11 ± 5.14