## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**RegionFeatures** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **All** \\ \hline _Frequency distribution by the number of features of eFBB_ & & & & & & & & & & & \\ Frontal & 2 & 11 & 15 & 20 & 18 & 12 & 7 & 5 & 4 & 1 & 95 \\ Temporal & 0 & 7 & 6 & 15 & 15 & 11 & 7 & 5 & 4 & 1 & 71 \\ Parietal & 0 & 0 & 8 & 0 & 2 & 3 & 3 & 3 & 3 & 0 & 16 \\ Occipital & 0 & 0 & 8 & 1 & 3 & 5 & 4 & 3 & 4 & 1 & 23 \\ Anterior cingulate & 0 & 0 & 4 & 7 & 7 & 6 & 6 & 3 & 4 & 1 & 38 \\ Posterior cingulate & 0 & 2 & 3 & 7 & 11 & 10 & 6 & 4 & 4 & 1 & 48 \\ \hline _Frequency distribution by the number of features of dFBB_ & & & & & & & & & & & \\ Frontal & 1 & 6 & 13 & 17 & 17 & 10 & 6 & 5 & 4 & 1 & 80 \\ Temporal & 1 & 7 & 16 & 21 & 17 & 13 & 7 & 5 & 4 & 1 & 92 \\ Parietal & 0 & 0 & 6 & 12 & 10 & 9 & 5 & 5 & 4 & 1 & 52 \\ Occipital & 0 & 0 & 1 & 3 & 6 & 4 & 3 & 4 & 2 & 1 & 24 \\ Anterior cingulate & 0 & 0 & 0 & 7 & 2 & 6 & 1 & 3 & 3 & 1 & 23 \\ Posterior cingulate & 0 & 0 & 0 & 0 & 0 & 2 & 1 & 0 & 0 & 1 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Frequency distribution according to the number of selected features in dual FBB. tional tests to obtain multimodal data are costly, time consuming, and delay treatment. Our study uses dual FBB to improve classification performance as if using multimodal data with one test. In addition, this study evaluated the influence of feature selection of each brain region on classification of eFBB and dFBB. This brain region SUVR constitutes the composite SUVR, and frontal and temporal regions are important when observing early and delayed amyloid regions. The priorities for the remaining regions differed for dFBB and eFBB. This means that eFBB and dFBB have different brain regions of focus. In the eFBB, the posterior cingulate had the third highest influence, but in the dFBB, it had the lowest influence among the six regions. According to previous studies, A\(\beta\) accumulation has the greatest effect on the temporal and parietal lobes in dFBB [32]. With the recognition that a single biomarker cannot provide diagnostic certainty when considering comorbidities and potential overlapping pathologies [33] and the increasing availability of medical artificial intelligence, the number of studies using multimodal data has increased [11; 12; 13]. However, to the best of our knowledge, no study has quantitatively analyzed dual-phase amyloid PET with ML models. Most studies using dual-phase amyloid have confirmed the time range of early amyloid PET, which showed the highest correlation with FDG [5; 6; 7; 34; 35; 36]. A classification study using dual-phase amyloid confirmed that readers could compare dual-phase amyloid PET and delayed amyloid PET [7]. Therefore, to the best of our knowledge, this paper is the first to classify amyloid PET images through ML using quantitative values of dual FBB images. The image of each phase was preprocessed so that the feature element represents a specific brain region, and the rank-based cumulative feature search, which evaluates the quality of the feature elements individually, was adopted and applied to the feature subsets representing each phase. That is, in the case of the ANOVA method for individual feature scores, only the main effect of each feature is considered. For dual FBB analysis, the two features were aggregated at the input level so that the classification model could analyze the features of both phases simultaneously. The method used in this study was designed to find subsets by evaluating the quality of individual features and arranging them according to their ranking. Therefore, one limitation of this method is that it does not consider the potential for attributes brought about by textures or nonlinear kernel mapping that appear in a subset of the original features.