A genetic optimized neural network for image retrieval in telemedicine

EURASIP Journal on Image and Video Processing

Table 3 Classification parameters for uncompressed and compressed images

Number of features	Parameters	Classifiers	Uncompressed		Compressed
			Feature extraction using FFT	Feature extraction using the proposed IRS-FFT	Feature extraction using FFT	Feature extraction using proposed IRS-FFT
20	Classification accuracy	MLP NN	88.46	92.31	90.38	92.31
	Classification accuracy	GOP NN	92.31	94.23	94.23	98.08
	Precision	MLP NN	0.92	0.96	0.86	1
	Precision	GOP NN	0.96	1	1	1
	Recall	MLP NN	0.85	0.89	1	0.86
	Recall	GOP NN	0.89	0.89	0.89	0.96
40	Classification accuracy	MLP NN	90.38	94.23	92.31	94.23
	Classification accuracy	GOP NN	94.23	96.15	94.23	98.08
	Precision	MLP NN	0.96	0.96	1	1
	Precision	GOP NN	1	1	1	1
	Recall	MLP NN	0.86	0.92	0.86	0.89
	Recall	GOP NN	0.89	0.93	0.89	0.96
60	Classification accuracy	MLP NN	88.46	90.38	92.31	94.23
	Classification accuracy	GOP NN	92.31	94.23	92.31	98.08
	Precision	MLP NN	0.92	0.96	1	1
	Precision	GOP NN	1	1	1	1
	Recall	MLP NN	0.85	0.86	0.86	0.89
	Recall	GOP NN	0.86	0.89	0.86	0.96
80	Classification accuracy	MLP NN	90.38	94.23	92.31	94.23
	Classification accuracy	GOP NN	94.23	96.15	98.08	98.08
	Precision	MLP NN	0.96	0.96	1	1
	Precision	GOP NN	1	1	1	1
	Recall	MLP NN	0.86	0.92	0.86	0.89
	Recall	GOP NN	0.89	0.93	0.96	0.96
100	Classification accuracy	MLP NN	90.38	94.23	92.31	94.23
	Classification accuracy	GOP NN	94.23	96.15	98.08	98.08
	Precision	MLP NN	0.96	0.96	1	1
	Precision	GOP NN	1	1	1	1
	Recall	MLP NN	0.86	0.92	0.86	0.89
		GOP NN	0.89	0.93	0.96	0.96