[Retracted] Deep Learning Model for Automatic Classification and Prediction of Brain Tumor

Table 1

Comparison of existing state-of-art models.

Citation/year of publishing	Reference	Approach	Objective	Challenges of the approach
[1]/2021	FIN	CDLLC-CNN, VGG19, VGG16	To develop brain tumor classification technique by using CDLLC on CNN.	Dataset contained 3064 brain tumor images. It implemented binary classification and yielded an accuracy of 96.39%.
[2]/2021	JAIHC	SVM-CNN, VGG16, VGG19	To distinguish brain tumor from healthy individuals using SVM with CNN.	Dataset contained 1426 brain tumor images. It implemented binary classification and yielded an accuracy of 95.82%.
[3]/2021	MMTA	RNGAP-CNN, DenseNet201, VGG16	To predict brain tumor from normal individual by RNGAP model on CNN.	Dataset contained 3064 brain tumor images. It implemented binary classification and yielded an accuracy of 97.08%.
[4]/2021	MRT	3DCNN, DenseNet201, VGG 16	To detect brain tumor on CT scans using 3DCNN technique.	Dataset contained 1074 brain tumor images. It implemented binary classification and yielded an accuracy of 92.67%.
[5]/2021	NCA	MSMCNN, DenseNet121, VGG19	To automatically classify CT images into brain tumor and normal individuals by using MSMCNN.	Dataset contained 374 brain tumor images. It implemented binary classification and yielded an accuracy of 96.36%.
[6]/2019	BS	HSANN, VGG19, DenseNet201	To classify BT by using HSANN architecture.	Dataset contained 3064 brain tumor images. It implemented binary classification and yielded an accuracy of 97.33%.
[7]/2017	SIVP	ELM-CNN, DenseNet201, VGG16	To develop an ELM system to early diagnose BT individuals.	Dataset contained 1074 brain tumor images. It implemented binary classification and yielded an accuracy of 97.8%.
[8]/2020	JDI	3DCNN, DenseNet201	To classify BT analysis by using 3DCNN	Dataset contained 1074 brain tumor images. It implemented binary classification and yielded an accuracy of 96.49%.
[9]/2021	JCS	Deep-CNN, DenseNet121, DenseNet201	To develop Deep-CNN system that can determine BT by using CT scans.	Dataset contained 121 brain tumor images. It implemented binary classification and yielded an accuracy of 94.58%.
[10]/2021	WMPBE	CNN, VGG16, VGG19, DenseNet201	To diagnose BT by using an ensemble system of CNN.	Dataset contained 3064 brain tumor images. It implemented binary classification and yielded an accuracy of 84.19%.