# Brain Tumor Analysis Using Deep Learning and VGG-16 Ensembling Learning Approaches
## 4 Proposed Research Methodology  
The study aimed to automate the detection of brain tumors in MRI brain scans. Our suggested strategy uses CNN and VGG 16 to detect brain tumors employing brain MRI data. According to [PERSON] [63], VGG 16 can be considered a contender for other optimization methods, such as AlexNet and Grid Search optimization, while detecting and classifying brain tumor MRI with CNN. Considering the rationale of [63,64] for the application of AI in diagnostics, the proposed framework was broken down into many steps. The brain MRI image was used as the primary input image. Data operations, including thresholding and refractive error, were carried out to reduce noise. The database of brain MRI images was analyzed and improved. The images were then resized for use as input to the model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Year** & **Methodology/ Approach** & **Dataset** & **Result** & **Drawback** \\ \hline \multirow{3}{*}{[20]} & \multirow{3}{*}{2018} & \multirow{3}{*}{\begin{tabular}{c} Classification D.N.N. and \\ Segmentation Using Fuzzy C \\ vs. affected. \\ \end{tabular} } & Harvard & \multirow{3}{*}{98\%} & The methodology used was \\  & & & & 44 standard images & not novel. \\ \hline \multirow{3}{*}{[21]} & \multirow{3}{*}{2018} & \multirow{3}{*}{2018} & \multirow{3}{*}{\begin{tabular}{c} data sets \\ CNN for grading \\ \end{tabular} } & \multirow{3}{*}{Accuracy of 98\% and 98\%} & \multirow{3}{*}{High learning rate} \\  & & & & \\  & & & & NDT 516 images) & \\ \hline \multirow{3}{*}{[22]} & \multirow{3}{*}{2020} & \multirow{3}{*}{\begin{tabular}{c} BRAIN NETs \\ For detect \\ And Classification \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} (BITE), Fig share \\ (4689 detection \\ and for classification) \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} 98\% accuracy \\ For detection \\ and 99\% \\ \end{tabular} } & \multirow{3}{*}{Complex architecture} \\  & & & & \\ \hline \multirow{3}{*}{[23]} & \multirow{3}{*}{2020} & \multirow{3}{*}{\begin{tabular}{c} BRATS, \\ CE-MRI \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} VGGnetAnd \\ KNN as classifier \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} 97.28\% and \\ 98.69\% on \\ Both of the datasets \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} No \\ training \\ testing time, \\ \end{tabular} } \\ \hline \multirow{3}{*}{[24]} & \multirow{3}{*}{2020} & \multirow{3}{*}{R.N.N.} & Private & Classification 9\% Specificity \\  & & & 98\% \\  & & & & 98\% \\  & & & comprising 1000 images & Sensitivity 97\% \\ \hline \multirow{3}{*}{[25]} & \multirow{3}{*}{2019} & \multirow{3}{*}{CNN} & Kaggle & Accuracy \\  & & & & 92.3\% \\ \hline \multirow{3}{*}{[26]} & \multirow{3}{*}{2020} & \multirow{3}{*}{\begin{tabular}{c} ELM- \\ LRF \\ CNN \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} Figshare dataset \\ (3064 images) \\ \end{tabular} } & \multirow{3}{*}{97\%} & \multirow{3}{*}{Small training data.} \\  & & & & \\ \hline \multirow{3}{*}{[27]} & \multirow{3}{*}{2020} & \multirow{3}{*}{CNN} & Kaggle & 90 to 99\% Accuracy & No preprocessing data \\ \hline \multirow{3}{*}{[28]} & \multirow{3}{*}{2019} & \multirow{3}{*}{\begin{tabular}{c} Residual \\ Network \\ ResNet \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} Figshare \\ (3064 images) \\ \end{tabular} } & \multirow{3}{*}{95\% Accuracy being achieved} & \multirow{3}{*}{Ambiguous data depiction} \\  & & & & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Proposed \\ Model \\ \end{tabular} } & \multirow{3}{*}{2022} & \multirow{3}{*}{
\begin{tabular}{c} CNN, VGG-16, \\ Ensemble Model \\ \end{tabular} } & \multirow{3}{*}{MRI dataset} & \multirow{3}{*}{96\%, 98.15\%, 98.41\%} & \multirow{3}{*}{High accuracy} \\ \cline{1-1} \cline{5-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._A VGG 16 pre-trained convolution layer was used to improve and increase classification accuracy into two classes: yes and no.