Table 5.

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

Author(s)	Number of images	Method	Validation	Results (%)
Saleh et al. [54]	4480	Data augmentation and CNN (Xception)	Hold-out 60:20:20	Acc. = 98.75
Kang et al. [55]	3000	Ensemble deep learning features (DenseNet169, ShuffleNet, and MnasNet), SVM	Hold-out 80:20	Acc. = 93.72
Shoaib et al. [56]	6517	Custom-designed CNN (BrainTumorNet)	Hold-out 75:25	Acc. = 93.15 Rec. = 93.14 Spe. = 97.72 Pre. = 93.14 Coh.Ka. = 81.74 F1 = 93.11
Khan et al. [57]	8298	Dataset (3 datasets combined), feature extraction (standard dev., entropy, energy, etc.), CNN	Hold-out 70:15:15	Acc. = 97.92 F1 = 98.0
Our method	3264	PFP-HOG, IChi2, kNN	10-fold cross-validation	Acc. = 94.67 Pre. = 94.41 Rec. = 93.84 F1 = 94.10

Acc. Accuracy, Pre precision, Rec. recall, F1 F1-score, Spe. Specificity, Coh.Ka. Cohen’s kappa