# A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease
## 2 Literature Review
In this context, the BO algorithm was employed for selecting optimal hyper-parameters of KNN, and this process significantly reduced the training time of the framework. [PERSON] et al. [28] designed a new framework for the early diagnosis of AD using MRIs. After the collection of 6400 MRIs, the informative features were extracted utilizing the CNN model. The extracted informative deep features were passed into the KNN to classify four types of AD classes. Four different evaluation measures were used for assessing the efficacy of the presented CNN-KNN framework. However, conventional classification models such as KNN have high computational cost, particularly when the size of the MRI dataset is larger. [PERSON] et al. [29] designed an efficient three-dimensional CNN model for AD detection. This presented CNN model integrated backpropagation-based explainability approaches including occlusion map and genetic algorithm for reducing time cost and improving the convergence rate by optimizing the search space. [PERSON] et al. [30] developed a lower-cost transfer-learning model named Mobile-Net for effective detection of AD. The developed Mobile-Net model's effectiveness was tested on different test cases and online datasets. [PERSON] et al. [31] designed a fuzzy-based VGG model to predict the stages of AD utilizing MRI images. In this context, the conventional three-dimensional CNN model and pre-trained CNN models (Mobile-Net and VGG) were not robust to differences in resolution and variations in imaging conditions. [PERSON] et al. [32] developed a hybrid deep learning model for precise recognition of AD. The developed hybrid model incorporated long short-term memory (LSTM) with the CNN, and this model effectively updated its batch size, Adam optimization, and learning weights for improving the classification accuracy. However, the combination of LSTM with the CNNintroduced more parameters that increased the risk of overfitting. [PERSON] et al. [33] initially performed automated anatomical labelling (AAL) for segmenting interested regions in collected sMRI images. Next, the informative features were extracted and passed into the CNN model for classifying AD classes. Finally, an online ADNI dataset was used for validating the efficiency of the presented model. Furthermore, [PERSON] et al. [34] developed a novel framework for AD detection. This framework incorporated Otsu thresholding technique, the fuzzy-based elephant-herding optimization algorithm, and a dual-attention-based multi-instance deep learning (DA-MIDL) model for the early diagnosis of AD. The developed framework obtained improved detection performance in comparison to the conventional models on the AIBL dataset. In this context, the CNN and DA-MIDL models consumed a larger amount of computational resources, which led to curse of dimensionality problems on high-dimensional image data such as those in the ADNI and AIBL datasets. As can be surmised from an overall review of the literature, MRI has been used to study the structure of the brain and to define diseased areas as well as to assess a wide range of neurological diseases. It is essential to identifying AD patients early on in order to set preventative measures in practice. Deep learning has made significant advances in medical image processing in recent years. With segmented MRI images, brain diseases can be classified more precisely because of detailed tissue-architecture analysis. Several complicated segmentation techniques that have been proposed for the diagnosis of AD have problems such as curse of dimensionality and overfitting. Deep learning techniques have drawn interest for application in the segmentation of the brain's structure and classification of AD because they can produce useful findings across an extensive data collection. In order to highlight the stated problems, a novel fuzzy-based superpixel-clustering algorithm and MGTO-CapsNet model were developed for accurate AD detection, particularly in the high-dimensional sMRI datasets.