## 6 Performance and Discussion
### Discussion  
Our result suggests the proposed method shows good classification results for AD vs. CN (ADD vs. NC), and the network can be trained from either database, as the result is good in both cases. However, in the case of CN vs. MCI (NC vs. mAD), the result is satisfactory on the trainee dataset, but poor on the non-traine dataset. This shows that the distinction between CN vs. MCI is unfavorable with the proposed method; further, on the fine analysis of feature, we found that the characteristics of the CN and MCI feature after Gaussian smoothing become indistinguishable in contrary to CN vs. AD, which may be the reason why the classifier cannot distinguish between them. In general AD  
Figure 10: Classification test result from BR-NN trained on ADNI or GARD dataset. For ideal classification, the accuracy, sensitivity, and specificity are all 100%. Training on both dataset features would overfit the data and might not produce a convincing result for an independent test, hence the dataset was trained independently. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**ADNI 70\% Dataset Trained Experiment**} & \multicolumn{2}{c}{ \begin{tabular}{c} **Training** \\ **Result** \\ **proposed** \\ **(ROI, CL+GS)** \\ **+ BER-N** \\ \end{tabular}  } & \multirow{2}{*}{**ROI, CL+BR-**} & \multicolumn{2}{c}{ \begin{tabular}{c} **Testing** \\ **Result** \\ **ROI, CL+BR-** \\ **ROI, CL+GS** \\ **NN** \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} **50\% Top** \\ **Features** \\ **proposed** \\ **ROI, LA+GS** \\ **+ BER-N** \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} **Training** \\ **Result** \\ **proposed** \\ **ROI, LA+GS** \\ **+ BER-N** \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} **Testing** \\ **Features** \\ **proposed** \\ **ROI, LA+GS** \\ **+ BER-N** \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} **50\% Top** \\ **Features** \\ **proposed** \\ **ROI, LA+GS** \\ **+ BER-N** \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} **Testing** \\ **Features** \\ **proposed** \\ **ROI, LA+GS** \\ **+ BER-N** \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} **50\% Top** \\ **Features** \\ **proposed** \\ **ROI, LA+GS** \\ **+ BER-N** \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} **Realistic** \\ **Features** \\ **proposed** \\ **ROI, LA+GS** \\ **+ BER-N** \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} **Realistic** \\ **Features** \\ **proposed** \\ **ROI, LA+GS** \\ **+ BER-N** \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} **Realistic** \\ **proposed** \\ **ROI, LA+GS** \\ **+ BER-N** \\ \end{tabular}  } & \multicolumn{2}{c}{\ \begin{tabular}{c} **Realistic** \\ **Features** \\ **proposed** \\ **ROI, LA+GS** \\ **+ BER-N** \\ \end{tabular}  } \\ \hline \multirow{4}{*}{ \begin{tabular}{c} ADNI Test \\ (AD vs. CN) \\ \end{tabular}  } & ACC & 97.78 + 1.7 & 70.22 + 7.20 & 88.67 + 0.7 & 99.56 + 0.49 & 98.22 + 4.99 & 62.89 + 4.45 & 78.22 + 2.78 & 98.51 + 1.48 \\ \cline{2-9}  & SEN & 100 \(\pm\) 0  
and CN are comparatively more discriminative than CN and MCI (this is our primary assumption), in the course of smoothing, the CN features tend more towards the CN mean value and so does AD features, so because of this, the MCI group (which lies between AD and CN) when smoothen loosens its MCI features and tends to follow either AD or CN features, depending on its feature weights. This is also highlighted by the result of the ROI_CL/ROI_AAL +BR-NN result column of Table 5, where the performance of all test conditions i.e., AD vs. CN and CN vs. MCI abruptly drops if Gaussian smoothing is not mediated before classification. Similarly, the test with half of the features results in poor accuracy for the non-trainee dataset i.e., GARD classification. [PERSON] et al.