Lung Cancer Classification Employing Proposed Real Coded Genetic Algorithm Based Radial Basis Function Neural Network Classifier

Table 8

Comparison of the network training parameters for the LIDC dataset.

Parametric measures	Hybrid Logistic Regression-Artificial Neural Network Approach [9]	Hopfield Neural Network & Fuzzy Clustering Approach [10]	Back Propagation Neural Network Approach [11]	SRGWO-ELM Approach	Proposed; RCGA-RBFNN; Approach
Norm	378.67	231.98	196.72	121.47	109.54
MSE Error	0.9231	0.9745	0.1120	0.0097	0.0089
Training Efficiency % Mean	86.25	81.26	94.57	98.83	99.25
Training Efficiency % STD	6.71	6.47	5.64	6.05	5.96
Testing Efficiency % Mean	90.35	88.61	95.84	97.12	98.96
Testing Efficiency % STD	6.13	6.09	5.45	6.03	6.01
Hidden neurons	19	7	12	5	3
Accuracy %	92.8	89.07	97.14	98.64	99.03
Time min	3.27	2.96	3.5	2.01	1.76