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Table 1 Studies conducted on brain tumor detection

From: MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques

References

Purpose

Model

Limitations/future works

Badža et al. [5]

To classify different types of brain tumors using a convolutional neural network

CNN

Examining the execution of the designed neural network in the mentioned study, as well as enhanced ones in different medical images

Gumaei et al. [23]

To classify brain tumors using a hybrid feature extraction method

RELM

Lack of comparison of the technique used in this study and other machine learning methods

Rehman et al. [22]

Proposing three architectures of convolutional neural networks (alexnet,Googlenet, and vggnet) to classify brain tumors

Convolutional neural networks (AlexNet, GoogLeNet, and VGGNet)

Explore other essential deep neural network’s architectures for brain tumor classification with less time complexity

Mittal et al. [29]

Using segmentation method to diagnose brain tumors using deep learning-based methods

Combination of SWT and GCNN

Other databases like PASCAL, Berkeley or BRATS can be used

It is recommended to use a variety of diverse classifiers to increase the accuracy of the classifier

Phaye et al. [24]

Provide an approach to improve outputs using a network with dense layers

Dense capsule networks (DCNet) and diverse capsule networks (DCNet++)

Computational complexity must be reduced to enhance classifier execution

Pashaei et al. [27]

Developing an algorithm for extracting and classifying features with the CNN and KELM

KELM

Not mentioned

Abiwinanda et al. [28]

Use the convolutional neural network to segment and classify brain tumors automatically

CNN

Pay attention to the color balancing step to improve the classifier's accuracy

Abd-Ellah et al. [15]

Brain tumor detection with a two-step automatic detection system

Preprocessing, feature extraction using CNN and classification with error-correcting output codes support vector machine (ECOC-SVM)

Not mentioned