# A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease
## 2 Literature Review  
The existing studies on the topic of AD detection are reviewed in this section. [PERSON] et al. [19] designed an efficient learning model named Deep Triplet Network (DTN) for AD detection. The DTN model utilizes a conditional loss function for improving the model's accuracy and overcoming problems related to lack of limited samples. The DTN model was inspired from the concept of a pre-trained model named VGG-16. The empirical evaluation conducted on an online dataset demonstrated the success of this DTN model in AD detection over the conventional models. [PERSON] et al. [20] presented a shallow convolutional neural network (CNN) model for the accurate and fast diagnosis of AD. This regularized model efficiently learns features from both larger and smaller datasets and outperformed other comparative models in diagnosing AD. Generally, AD detection needs a higher level of discriminative power for differentiating subtle changes in brain structure. However, the DTN and shallow CNN models are not adaptable to various modalities related to other complex deep learning models. [PERSON] and [PERSON] [21] combined the arithmetic optimization algorithm (AOA) with the VGG-16 model for classifying the types of AD. In the pre-processing phase, a cat12 toolkit was initially utilized for processing the format of t1-weighted MRI images, and further, a linear contrast stretching was employed for enhancing the contrast level of MRI images. Next, an adaptive RoI model was applied for segmenting the brain nodules. Finally, an optimized VGG-16 model was introduced for classifying the AD classes, such as late dementia, mild dementia, and normal. In this study, an AOA was integrated with the VGG-16 model for optimizing the batch size and dropout rate and overcame problems such as computational time and data imbalance. [PERSON] [22] used two pre-trained models named ResNet-50 and AlexNet for classifying the classes of AD. The experimental outcomes indicated that the AlexNet model obtained significant detection accuracy in comparison to the ResNet-50 model. [PERSON] et al. [23] presented a hybrid classical quantum model (combination of ResNet-34 and classical neural networks) for classifying non-demented and demented MRI images for the early diagnosis of AD. However, the pre-trained models such as AlexNet, ResNet-34, and VGG-16 models were relatively deeper; therefore, there was increased risk of overfitting. [PERSON] et al. [24] employed three pre-trained models including ResNet-18, AlexNet, and GoogleNet for classifying four types of AD classes. As illustrated by their results, the ResNet-18 model obtained a maximum detection accuracy in comparison to other pre-trained models (AlexNet and GoogleNet). Furthermore, [PERSON] and [PERSON] [25] utilized the ResNet-18 model for extracting deep features from MRI datasets, which were further transferred to the random forest, support vector machine (SVM), and softmax classifier for medical image classification. The use of individual pre-trained models such as the ResNet-18 model has limited feature extraction ability that leads to performance degradation in larger MRI datasets. [PERSON] et al. [26] developed an efficient framework for the early recognition of AD. In this framework, the modified AlexNet, bag of features, and speeded up robust features (SURF) were integrated for extracting deep and local features from MRIs. These extracted features were passed into the SVM for classifying AD classes. However, the use of conventional machine learning classifiers such as SVM are prone to overfitting and outlier problems. [PERSON] et al. [27] presented an automated framework for AD detection by integrating CNN, the Bayesian optimization (BO) algorithm, and K-Nearest neighbour (KNN). Firstly, deep features were extracted from MRIs by implementing a CNN model, and further, these features were fed into the KNN for classifying AD and healthy subjects.