# A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease
## 1 Introduction  
The brain is an integral and important part of the human body, controlling the entire central nervous system [1]. It performs tasks such as emotional response, memory and learning, creative visualization, coordination, movement, and thinking [2]. Several stimuli affect the human brain and result in neurological and psychiatric disorders such as depression, AD, epilepsy, etc. [3, 4]. AD is a common brain disorder, and it leads to problems such as abnormal behaviour, memory, and thinking. Loss of neuron connection, abnormal clumps, and tangled fibre bundles are the major features of AD [5]. It is necessary to create an automatic detection system for better prognosis of AD. Different imaging techniques such as electroencephalography, computed tomography, positron emission tomography, genotyping, histopathology, MRI, etc. are used for the early detection of AD/dementia [6, 7]. Among the available imaging techniques, structural MRI is efficient because it clearly shows cerebral and cortical atrophy in patients with AD [8, 9, 10].  
In recent times, several machine-learning-based computer vision models have been developed by researchers for classifying the classes of AD, namely naive Bayes, decision tree, AdaBoost, random forest, etc. [11; 12; 13]. These conventional machine-learning models are efficient in image classification but have issues such as overfitting and outliers [14; 15]. Therefore, the deep-learning-based models have gained more attention among researchers and brought several advancements and improvements in applications such as image processing, computer vision, pattern recognition, and medical imaging [16]. However, it is challenged in distinguishing the dissimilar stages of AD, and this procedure is also expensive and tedious [17; 18]. The primary aim of this study was to diagnose AD with the help of deep learning models and transfer learning. The automatic AD detection reduces the cost and involvement of radiologists. The important contributions of this work are described below:  
* Implementation of a fuzzy-based superpixel-clustering algorithm for RoI segmentation on a real-time dataset and two online datasets (AIBL and ADNI). The fuzzy-based superpixel-clustering algorithm effectively groups image pixels with similar properties that helps in preserving structural information and spatial coherence of brain regions. In this context, the fuzzy-based superpixel-clustering algorithm is vital for precisely analysing and finding the structural changes related to AD.
* We performed hybrid feature extraction by the integrating PLTP, ResNet-50, and VGG-16 models. A broader range of feature information is captured by incorporating diverse features, and it leads to increased discriminative power and results in better classification of different classes.
* We propose a MGTO algorithm to select informative features from total extracted features. In this MGTO algorithm, a QRBL process is introduced to further improve the optimal position's quality. In the context of image classification, the feature selection not only improves the classification performance but also decreases the processing time.
* We implemented a CapsNet model for classifying the types of AD. In comparison to the traditional CNN models, the CapsNet model delivers more interpretable representations for image classification. Eight different evaluation measures are utilized for validating the performance of the fuzzy-based superpixel-clustering algorithm and the MGTO-CapsNet model.  
The remainder of this paper is structured as follows. A review of the existing literature is provided in Section 2. The details about the methods undertaken, results obtained, conclusion, and future scope are stated in Sections 3-5.