High-Performance Method for Brain Tumor Feature Extraction in MRI Using Complex Network

Table 5

Accuracy comparison.

Paper	Feature extraction method	Number of features	High accuracy (%)
Sarkar et al. [8]	Genetic algorithm	13 features	98.30
Hamid et al. [9]	GLCM	5 features	95.00
Ansari et al. [10]	GLCM and DWT	12 features	98.91
Li et al. [11]	Gabor transform, texture, and DWT	80 best-ranked features	88.00
Alves et al. [12]	Genetic algorithm, GLCM, GLRL, and DWT	Five best-ranked features	82.70
Kang et al. [13]	CNN	Top-3 deep features	98.50
Jena et al. [14]	Genetic algorithm, GLCM, GLRL, and DWT	471 features	97
Han et al. [22]	Complex network + wavelet transform	4 features	93.06
Nanmaran et al. [15]	Discrete cosine transform	6 features	96.8
Susanto et al. [16]	GLCM and DWT	16 features	98.65
Aamir et al. [17]	Multiple deep neural networks	Deep features	98.98
This study	Complex network	3 features	99.84

GLCM, gray-level co-occurrence matrix.