# A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease
## 5 Conclusions  
The accurate detection of AD assists clinicians in early diagnosis via the selection of efficient treatment options. A novel deep learning framework is introduced in this paper for accurate AD detection; this framework comprises two important steps: segmentation and classification. After acquiring sMRI images from AIBL, real time and ADNI datasets, a fuzzy-based superpixel-clustering algorithm segments the RoIs. Next, hybrid feature extraction (combination of PLTP, ResNet-50, and VGG-16 models) is carried out to extract informative deep features from segmented RoI images. These extracted deep features are dimensionally decreased via the selection of the most informative features by utilizing the MGTO algorithm. The obtained features are finally passed as input into the CapsNet model for categorizing the classes of AD. Eight different evaluation measures, namely accuracy, JSC, sensitivity, fallout, phi coefficient, PA, specificity, and DSC were used for investigating the performance of both the fuzzy-based superpixel-clustering algorithm and MGTO-CapsNet model. The fuzzy-based superpixel-clustering algorithm obtained a PA of 0.96, 0.94, and 0.98, and the MGTO-CapsNet model obtained a classification accuracy of 99.88%, 96.38%, and 99.94% on the ADNI, real-time, and AIBL datasets, respectively.  
However, the computational complexity of the proposed deep learning framework is higher due to it performing several steps; therefore, a significant unsupervised learning model will be proposed in future work. Furthermore, we will analyse the feasibility of the MGTO-CapsNet model in the diagnosis of other brain diseases. Additionally, we will collaborate with healthcare organizations or research institutions to integrate this proposed framework into their projects focused on AD. This will contribute to the ongoing efforts in better understanding and treating AD.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Processing Time (s)**} \\ \hline
**Model** & **ADNI Dataset** & **Real-Time Dataset** & **AIBL Dataset** \\ \hline MGTO-CNN & 76.54 & 96.44 & 80.28 \\ MGTO-GoogleNet & 68.22 & 88.52 & 70.35 \\ MGTO-VGG 16 & 60.56 & 80.49 & 48.30 \\ MGTO-AlexNet & 55.10 & 77.50 & 44.54 \\ MGTO-ResNet 50 & 50.44 & 70.25 & 42.30 \\ MGTO-DBN & 48.58 & 65.46 & 36.38 \\ MGTO-CapsNet & 42.16 & 60.33 & 32.55 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Processing time of the conventional models and the proposed MGTO-CapsNet model.  
**Author Contributions:** Investigation, resources, data curation, visualization, and writing--original draft preparation, review, and editing, P.G.; study conceptualization and software, G.P.R. and Y.A.; validation and formal analysis, methodology, supervision, project administration, and funding acquisition, C.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.