Skin Cancer Detection Using Kernel Fuzzy C-Means and Improved Neural Network Optimization Algorithm

Table 2

The simulation results of the comparative algorithms on the studied benchmark functions, moth-flame optimization (MFO) algorithm [28], world cup optimizer (WCO) [19], and the original neural network algorithm (NNA).

Algorithm
MVO [27]	Min	17.58	0.0024	0.0031	19.38	0	6.37e − 8
	Max	7.29e + 3	436.15	3.86e + 5	2.37e + 3	5.39e − 7	4.19e − 8
	Mean	2.96e + 3	280.16	4.19e + 4	67.25	6.15e − 9	1.27e − 7
	Std	5.79e + 2	95.37	6.37e + 4	11.62	3.19e − 8	3.07e − 7
MFO [28]	Min	28.14	2.57	0.051	6.29	4.38e − 6	8.39
	Max	129.08	4.18	9.27	53.49	0.015	110.35
	Mean	80.46	23.09	4.52	30.17	0.02	35.08
	Std	22.46	1.64	1.73	3.28	0.01	24.63
WCO [19]	Min	7.39	5.13e − 5	5.92e − 5	2.57	1.94e − 8	6.38e − 9
	Max	1.38e + 2	0.264	0.017	4.12	7.51e − 8	7.29e − 8
	Mean	105.37	0.0573	0.024	3.28	6.48e − 9	1.18e − 9
	Std	14.83	0.0873	3.28e − 5	1.23	4.29e − 9	3.19e − 8
NNA [29]	Min	4.91	6.29e − 21	9.75e − 9	0.017	9.37e − 16	5.9e − 17
	Max	45.22	34.39e − 18	1.19e − 8	4.62	2.58e − 12	5.19 e − 16
	Mean	13.83	6.30e − 18	5.94e − 8	0.532	3.42e − 13	6.67e − 16
	Std	5.29	3.62e − 21	6.53e − 9	0.42	1.97e − 13	7.50e − 17
INNA	Min	5.16	15.26e − 22	6.37e − 11	6.38e − 15	7.26e − 7	6.71e − 39
	Max	111.57	6.76e − 20	4.29e − 10	3.95e − 14	0.0234	9.43e − 35
	Mean	24.13	2.48e − 20	1.17e − 10	1.09e − 14	3.11e − 5	4.57e − 6
	Std	12.28	6.19e − 21	5.64e − 11	9.64e − 15	11.97e − 7	9.55e − 36