# Brain Tumor Analysis Using Deep Learning and VGG-16 Ensembling Learning Approaches
## 3 Comparative Analysis
### Existing Methodology
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Year** &
\begin{tabular}{c} **Methodology/** \\ **Approach** \\ \end{tabular} & **Dataset** & **Result** & **Drawback** \\ \hline
[60] & 2020 & CNN & \
\begin{tabular}{c} Fig-share Total \\ Images (3064) \\ from 233 patients) \\ \end{tabular} & 87\% and 92\% &
\begin{tabular}{c} No training time has \\ been mentioned \\ \end{tabular} \\ \hline
[51] & 2019 &
\begin{tabular}{c} PNN \\ Classification CNN \\ \end{tabular} & KaggleTCIA & 90\% Accuracy & Lack of comparative analysis \\ \hline
[52] & 2019 & R-CNN And SVM & Pvt Dataset & 95\% Accuracy &
\begin{tabular}{c} Rapid conversion \\ convolutional \\ feature map into the \\ region proposed \\ \end{tabular} \\ \hline
[53] & 2020 & \
\begin{tabular}{c} Inception Pre-trained \\ CNN \\ \end{tabular} &
\begin{tabular}{c} BRATS \\ 13,14,17,18 \\ \end{tabular} & 92\% Classification & Complex approach \\ \hline
[54] & 2019 & \begin{tabular}{c} ResNet-50 for Detection \\ GAN for Data Augmentation \\ \end{tabular} & BRATS 2016 & \
\begin{tabular}{c} ResNet87\% accuracy \\ With GAN, 92\% \\ \end{tabular} &
\begin{tabular}{c} Certain major aspects are \\ not mentioned \\ \end{tabular} \\ \hline
[55] & 2019 & CNN & \begin{tabular}{c} A private dataset \\ comprising 330 images \\ \end{tabular} & \
\begin{tabular}{c} Accuracy 98\% \\ with low Complexity level \\ \end{tabular} &
\begin{tabular}{c} Lower-level data \\ implementation. \\ \end{tabular} \\ \hline
[56] & 2019 & CNN &
\begin{tabular}{c} Fig-share \\ dataset (3064 images) \\ \end{tabular} & 96\% & Lacking comparative analysis \\ \hline
[57] & 2019 & 3D-Multi CNNs & BRATS 2018 & \
\begin{tabular}{c} Coefficient \\ 84\% \\ Sensitivity \\ 82\% \\ Specificity 99\% \\ \end{tabular} &
\begin{tabular}{c} Ambiguous results and no \\ exceptions carried out \\ \end{tabular} \\ \hline
[61] & 2019 & \begin{tabular}{c} Alexnet, \\ VGG-16 \\ \end{tabular} & \begin{tabular}{c} BRATS \\ 2015 \\ \end{tabular} & \
\begin{tabular}{c} VGG16 \\ gives 98\% \\ accuracy \\ \end{tabular} &
\begin{tabular}{c} There is no information about \\ enhancement other \\ than normalization. \\ \end{tabular} \\ \hline
[56] & 2016 & \begin{tabular}{c} D.N.N. \\ and \\ ELM \\ \end{tabular} & \begin{tabular}{c} 1000 \\ Images private \\ Dataset f om some \\ Indian hospital \\ \end{tabular} & \
\begin{tabular}{c} D.N.N. showed \\ 88\% accuracy \\ E.L.M. \\ \end{tabular} &
\begin{tabular}{c} Complex features \\ extraction process \\ extraction process \\ \end{tabular} \\ \hline
[59] & 2017 & \begin{tabular}{c} Convnet and slicenet \\ and VGNet \\ \end{tabular} & \begin{tabular}{c} BRATS 2017 \\ And TCIA \\ \end{tabular} & \
\begin{tabular}{c} 97\% Accuracy \\ being achieved \\ \end{tabular} &
\begin{tabular}{c} Scheme + high \\ training time \\ \end{tabular} \\ \hline
[19] & 2017 & 3D CNN & \begin{tabular}{c} BRATS \\ 2015 \\ \end{tabular} & \
\begin{tabular}{c} 75.4\% for \\ Flair 1.3\% \\ improvement **74.2\%** \\ with 3.3\% \\ improvement \\ \end{tabular} &
\begin{tabular}{c} Limited data on studies \\ previously done \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative Analysis. Similarly, no efficiency statistic constitutes a universal standard for all studies. The researchers used a set of performance assessments, but the interpretations and mathematical depiction of performance indicators in Table 1, and the findings in Table 1, were decisive. They are all related to the accuracy of parameter estimates in some way. When it comes to detection, excellent accuracy and performance are essential. As a result, it was found that the critical concern of the specialists remained accuracy. However, we know that MRI scans have an imbalanced class issue, so that that accuracy would not help much. Various ML techniques are available that can improve model performance despite imbalanced data, such as focal loss techniques, SMOTE (Synthetic Minority Over-sampling Technique), and class-weight [62]. When there is a class discrepancy, precision, and recall work well.