A multilevel features selection framework for skin lesion classification

Akram, Tallha; Lodhi, Hafiz M. Junaid; Naqvi, Syed Rameez; Naeem, Sidra; Alhaisoni, Majed; Ali, Muhammad; Haider, Sajjad Ali; Qadri, Nadia  N.

doi:10.1186/s13673-020-00216-y

Table 8 Performance comparison of state-of-the-art classifiers on selected datasets using set of performance measures

From: A multilevel features selection framework for skin lesion classification

Classifier	Dataset				Performance Measures
Classifier	I	II	III	IV	OA (%)	Recall	Precision	FNR	FPR	AUC	Time (sec)
Fine Tree	\(\checkmark \)				87.5	83.0	80.5	12.5	0.17	0.84	0.76
		\(\checkmark \)			79.1	78.5	79.0	20.9	0.22	0.73	0.93
			\(\checkmark \)		82.6	72.9	72.9	17.4	0.34	0.69	1.87
				\(\checkmark \)	89.3	77.9	78.9	10.2	0.20	0.73	5.04
Medium Tree	\(\checkmark \)				87.5	83.0	80.5	12.5	0.17	0.84	0.58
		\(\checkmark \)			79.1	78.5	79.0	20.9	0.22	0.73	0.76
			\(\checkmark \)		84.2	74.9	74.4	15.8	0.32	0.72	1.70
				\(\checkmark \)	87.8	69.4	75.4	11.7	0.29	0.73	2.16
Coarse Tree	\(\checkmark \)				86.9	78.0	80.0	13.1	0.22	0.77	0.59
		\(\checkmark \)			80.4	79.0	80.5	19.6	0.22	0.74	0.73
			\(\checkmark \)		87.1	70.9	71.4	12.9	0.36	0.67	1.60
				\(\checkmark \)	87.5	68.4	74.4	12	0.30	0.67	2.06
Linear SVM	\(\checkmark \)				93.1	87.5	90.5	6.9	0.12	0.97	0.60
		\(\checkmark \)			88.7	88.0	88.5	11.3	0.13	0.91	0.78
			\(\checkmark \)		95.2	80.9	90.9	4.8	0.26	0.91	1.78
				\(\checkmark \)	91.3	72.9	84.9	8.2	0.25	0.84	4.3
Quadratic SVM	\(\checkmark \)				95.6	93.0	93.5	4.4	0.07	0.99	0.61
		\(\checkmark \)			90.9	90.5	91.0	9.1	0.10	0.93	0.78
			\(\checkmark \)		96.2	87.9	91.9	3.8	0.19	0.94	1.71
				\(\checkmark \)	94.0	82.4	88.4	5.5	0.20	0.88	4.5
Cubic SVM	\(\checkmark \)				96.9	93.5	97.0	3.1	0.07	1.00	0.61
		\(\checkmark \)			91.8	91.5	91.5	8.2	0.09	0.96	0.78
			\(\checkmark \)		96.6	89.4	92.9	3.4	0.18	0.95	1.69
				\(\checkmark \)	95.6	87.4	89.4	3.9	0.21	0.90	5.42
F-KNN	\(\checkmark \)				\(98.8*\)	97.0	99.0	1.2	0.03	0.97	0.60
		\(\checkmark \)			\(99.2*\)	99.0	99.0	0.8	0.02	0.94	0.73
			\(\checkmark \)		\(97.1*\)	93.9	93.4	2.9	0.13	0.88	1.62
				\(\checkmark \)	95.8	92.9	92.4	4.6	0.19	0.86	2.53
Medium KNN	\(\checkmark \)				92.5	81.5	95.5	7.5	0.19	1.00	0.63
		\(\checkmark \)			91.8	91.0	91.5	8.2	0.10	0.96	0.72
			\(\checkmark \)		95.5	78.9	91.9	4.5	0.28	0.90	1.50
				\(\checkmark \)	89.3	62.4	91.4	10.2	0.35	0.86	2.15
Weighted KNN	\(\checkmark \)				93.1	83.0	96.0	6.9	0.17	1.00	0.62
		\(\checkmark \)			94.4	93.5	95.0	5.6	0.07	0.98	0.72
			\(\checkmark \)		95.2	80.9	90.9	4.8	0.26	0.92	1.60
				\(\checkmark \)	94.1	75.9	87.9	5.4	0.22	0.91	2.12
Ensemble BT	\(\checkmark \)				80.0	50.0	40.0	20	0.20	0.90	0.78
		\(\checkmark \)			82.6	82.0	82.5	17.4	0.19	0.83	3.47
			\(\checkmark \)		93.2	81.4	86.4	6.8	0.26	0.85	7.87
				\(\checkmark \)	92.3	73.4	88.9	7.2	0.25	0.87	13.48
Ensemble S-KNN	\(\checkmark \)				98.1	95.5	99.0	1.9	0.04	1.00	4.06
		\(\checkmark \)			96.2	96.0	96.0	3.8	0.05	0.99	3.49
			\(\checkmark \)		96.8	89.9	91.4	3.2	0.17	0.92	5.36
				\(\checkmark \)	\(95.9*\)	93.4	95.4	3.6	0.17	0.92	7.59
Ensemble RUSB	\(\checkmark \)				88.8	86.0	81.5	11.2	0.14	0.93	4.74
		\(\checkmark \)			85.7	85.0	85.5	14.3	0.16	0.88	5.24
			\(\checkmark \)		85.5	87.9	83.4	14.5	0.21	0.88	7.09
				\(\checkmark \)	83.3	82.4	74.9	16.2	0.20	0.85	9.45

* Shows the highest value in each dataset

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