The impact of image augmentation techniques of MRI patients in deep transfer learning networks for brain tumor detection

Journal of Electrical Systems and Information Technology

Table 1 Comparing the findings to previous related works

Paper	Technique	Accuracy percentage
[37]	GLCM	82.27
[38]	CNN	84.19
[39]	SVM	85.00
[40]	VGG19	90.28
[41]	CapsNet	90.89
[40]	BoW-SVM	91.28
[42]	DWT-Gabor-NN	91.90
[43]	CNN-ELM	93.68
[44]	VGG-16	94.42
[45]	DWT-DNN	96.27
[18]	MobileNetV2	96.88
[19]	AlexNet	98.24
[46]	Dense-Net classifier	98.26
[46]	Dark-Net classifier	96.52
[47]	2D CNN	96.47
[47]	Auto-encoder network	95.63
[48]	CNN	93.3
[49]	Momentum	97.71
	SMP-SGD	96.12
	SMP-Momentum	96.04
	SMP-Adagrad	97.35
	SMP-Adam	96.49
[50]	VGG16	95.11
	InceptionV3	93.88
	VGG19	94.19
	ResNet50	93.88
	InceptionResNetV2	93.58
	Xception	94.5
	IVX16	96.9
Models with applied data augmentation	InceptionV3	98.44
	VGG16	96.88
	DenseNet169	96.88