Table 6.

Comparison of results (%) obtained with the prior state-of-art methods developed for brain gliomas using MRI datasets

Author(s)	Number of images	Method	Validation	Results (%)
Raghavendra et al. [58]	800	Method 1: elongated quinary patterns and entropy analysis (EQPE), kNN Method 2: deep learning (VGG16), kNN	10 fold cross-validation	Method 1: Acc. = 92.5 Sen. = 90.66 Spe. = 93.6 Method 2: Acc. = 94.25 Sen. = 94.33 Spe. = 94.20
Hsieh et al. [59]	107	Histogram moments, local statistics (correlation, entropy, energy, etc.), logistic regression	Leave-one-out cross-validation	Acc. = 88.0 Sen. = 82.0 Spe. = 90.0
Anaraki et al. [60]	9035	Data augmentation, dataset (4 datasets combined), genetic algorithm, and CNN	Hold-out 80:20	Acc. = 90.9
Sert et al. [61]	200	Single image super-resolution, deep learning (ResNet), and SVM	5-fold cross validation	Acc. = 95.0 Auc. = 0.98
Banerjee et al. [62]	2043	CNN (PatchNet, SliceNet, and VolumeNet)	Leave one patient out	Acc. = 97.19
Our method	8328	PFP-HOG, IChi2, kNN	10-fold cross-validation	Acc. = 98.19 Pre. = 98.18 Rec. = 98.19 F1 = 98.19

Acc. Accuracy, Sen. Sensitivity, Spe. Specificity, Rec. recall, Pre. Precision, F1 F1-score, Auc. area under curve