Table 2.

Overview of papers using deep learning for brain tumor classification.

Study	Method	Proposed Solution and Preprocessing Approach	Software’s/Tools/Languages/ Libraries used for Simulation and Implementation	Evaluation
Subhashis Banerjee et al. [67].	Deep Convolutional Neural Networks (ConvNets) using multi-sequence MR images.		Terser flow and Python	Accuracy = 97%
Yufan Zhou et al. [68].	Convolutional Neural Networks	DenseNet-RNN, DenseNet-LSTM, DenseNet-DenseNET	Tensor Flow, Nvidia Titan Xp GPU	Accuracy = 92.13%
Nyoman Abiwinanda et al. [69].	Convolutional Neural Network	AlexNet,VGG16,ResNet	Matlab	Accuracy = 84.19%
Esther Alberts et al. [70].	SVM, RF, KNN, LOG, MLP and PCA	LBP, BRIEF and HOG	Not Mention	Accuracy = 83%
Ali ARI & Davut HANBAY [71]	Convolutional Neural Network	ELM-LRF	MATLAB 2015	Accuracy = 97.18%
Yota Ishikawaet et al. [73].	Deep Convolutional Neural Networks	BING objectness estimation, Voronoi diagram, Binarization, Watershed transform	Not Mention	Accuracy = 98.5%
Heba Mohsen et al. [74].	Deep Neural Network	Discrete Wavelet Transform (DWT), Principal Components Analysis (PCA)	MATLAB R2015a and Weka 3.9	Accuracy = 96.97%
Justin S. Paula et al. [75].	Convolutional Neural Network, Fully Connected Neural Network, Random Forests		Not Mention	Accuracy = 91.43%
Yan Xu et al. [76].	Deep Convolutional Activation Features	Deep Convolutional Activation Features trained by ImageNet knowledge	Not Mention	Accuracy = 97.5%
Parnian Afshar et al. [96].	Convolutional Neural Networks(CNNs)	Capsule Networks (CapsNets)	Python 2.7 and Keras library	Accuracy = 86.56%