Lung Cancer Diagnosis Based on an ANN Optimized by Improved TEO Algorithm

Table 6

The optimum features using the proposed ITEO applied to GLCM.

Image #	Autocorrelation	Correlation	Energy	Homogeneity	Inverse difference	Highest probability	Sum average	Class
1	1.163	0.97	0.8911	0.9972	0.9971	0.9417	2.1095	Yes
2	1.407	0.9807	0.749	0.9966	0.9965	0.8594	2.2728	Yes
3	1.167	0.9827	0.8903	0.9979	0.9978	0.9411	2.1119	Yes
4	1.669	0.976	0.6421	0.9948	0.9947	0.769	2.4488	Yes
5	1.054	0.9437	0.9595	0.997	0.997	0.969	2.036	Yes
6	1.02	0.9705	0.986	0.9985	0.9984	0.9931	2.0131	Yes
7	1.111	0.9603	0.9246	0.9993	0.9972	0.9611	2.0748	Yes
8	1.121	0.954	0.9064	0.9988	0.9987	0.9512	2.0955	Yes
9	1.135	0.975	0.9105	0.9996	0.9995	0.9534	2.0928	Yes
10	1.112	0.9413	0.921	0.9985	0.9984	0.959	2.0782	Yes
11	1.164	0.9636	0.891	0.997	0.998	0.94	2.1127	Yes
12	1.177	0.9476	0.879	0.996	0.997	0.9351	2.121	Yes
13	1.404	0.9701	0.7565	0.9971	0.995	0.859	2.2727	Yes
14	1.341	0.9648	0.784	0.9952	0.9951	0.8774	2.2339	No
15	1.403	0.9706	0.756	0.9954	0.9953	0.8585	2.2723	No
16	1.251	0.9784	0.8405	0.9971	0.997	0.9142	2.1682	No
17	1.263	0.9473	0.8284	0.9945	0.9944	0.9062	2.1787	No
18	2.426	0.961	0.4823	0.9907	0.9907	0.5114	2.9592	No
19	1.067	0.976	0.9541	1.0001	1	0.9767	2.0473	No
20	2.113	0.9718	0.5192	0.994	0.9939	0.6199	2.7485	No