Table 6.

Performance analysis of hybridized features in lung nodule classification

Model	Explanation	Accuracy (%)	Sensitivity (%)	Specificity (%)	Precision (%)	F1 score	FPR (%)	FNR (%)
Model 25	VGG16 + HOG + logistic regression	78.66	80.83	77.34	68.39	0.74	22.66	19.16
Model 26	VGG16 + HOG + linear SVM	78.43	82	76.25	67.69	0.74	23.74	17.98
Model 27	VGG16 + HOG + RBF-SVM	82.46	54.35	99.5	98.56	0.7	0.005	45.6
Model 28	VGG16 + HOG + random forest	73.88	31.22	99.76	98.75	0.47	0.002	68.77
Model 29	VGG19 + HOG + logistic regression	76.56	87.15	70.14	63.9	0.74	29.85	12.84
Model 30	VGG19 + HOG + linear SVM	76.49	85.38	71	79.37	0.82	28.89	14.89
Model 31	VGG19 + HOG + RBF-SVM	93.28	89.5	95.6	92.43	0.91	4.44	10.5
Model 32	VGG19 + HOG + random forest	73.65	30.63	99.76	98.72	0.47	0.002	69.37
Model 33	ResNet50 + HOG + logistic regression	82.9	89.72	78.77	71.94	0.79	21.2	10.27
Model 34	ResNet50 + HOG + linear SVM	79.6	95.8	69.78	65.8	0.78	30.2	4
Model 35	ResNet50 + HOG + random forest	88.13	84.78	90.16	83.95	0.84	9.8	15.2
Model 36	ResNet50 + HOG + RBF-SVM	97.53	98.62	96.88	95.04	0.97	3.12	1.38

FPR: False-positive rate, FNR: False-negative rate, RBF: Radial basis function, SVM: Support vector machine, HOG: Histogram of oriented gradients