Alzheimer’s Disease (AD) is a neurological condition that affects a large number of people.”Brain atrophy is a result of this neurological condition, which can result in memory loss, cognitive disabilities, and death. In its early stages, AD is difficult to identify. Thus, an early diagnosis and effective treatment of AD are more beneficial and create fewer complexities. AD is a common brain condition that is hard to recognize, and its classification procedures require a biased representation of traits to distinguish similar brain patterns. Multimodal neuro-image integrates numerous clinical images, which assist in identifying and diagnosing AD with better accuracy and effectiveness. Here, an efficient deep learning-based technique is developed to detect AD from“the multi-modal data.”Online resources are used to collect the necessary multimodal data, which includes Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and genetic data in the beginning. In this study, the developed model used 89 affected subjects and 90 healthy control subjects. Then, the collected MRI, PET, and genetic data are fed into the preprocessing stage. Here, genetic data is pre-processed using the data-filling approach. Likewise, the “MRI and PET images are used for the Regions of Interest (ROI) segmentation phase, and it is executed via Dilated TransUNet (DTUNet). To reduce processing time, the ROI segmentation process removes specific regions from MRI and PET images. The pre-processed MRI, PET, and genetic data are passed to the Graph Convolutional Networks (GCNs) to extract the features. From the extracted features, the relevant features are selected using the Enhanced Secretary Bird Optimization (ESBO) algorithm, and this feature is multiplied by the optimal weight selected that is selected using the same ESBO to form the weighted fused features. The final stage involves giving the weighted fused features to the Adaptive Deep CapsNet (ADCapsNet) to diagnose AD. Here, the effectiveness of the ADCapsNet is also enhanced by tuning the parameters using ESBO. Finally, the analysis procedures are executed to prove the proposed framework’s efficiency. In the evaluation, the accuracy of the developed model is 95.17, which is 7.11%, 2.3%, 4.3%, and 1.46% better than “Convolutional Neural Networks (CNNs), Residual Neural Network (ResNet), Visual Geometry Group 16 (VGG-16), and Deep CapsNet (DcapsNet).” The developed approach’s ability to detect AD at an early stage is demonstrated in the experimental outcome, which allows for better planning and improved quality of life for individuals.
Bagade et al. (Thu,) studied this question.