Development of hybrid deep CNN with denoising MLP for accurate prediction of Alzheimer’s disease

Abstract

Alzheimer’s disease (AD) prediction is a critical task in the field of healthcare, and researchers have been exploring various techniques to improve its accuracy. This research paper focuses on the major contributions of a hybrid deep convolutional neural network (CNN) with denoising using a multilayer perceptron (MLP) and pooling layers in AD prediction. The proposed hybrid model leverages the power of deep CNNs to extract meaningful features from molecular or imaging data related to AD. The model incorporates denoising techniques using MLP to enhance the quality of the input data and reduce noise interference. Additionally, pooling layers are employed to summarize the extracted features and capture their essential characteristics. Several experiments and evaluations were conducted to assess the performance of the proposed model. Comparative analyses were carried out with other techniques such as PCA, CNN, Resnet18, and DCNN. The results were presented in a comparison chart, highlighting the superiority of the hybrid deep CNN with denoising and pooling layers in AD prediction. The research paper further discusses the accuracy, precision, and recall values obtained through the proposed model. These metrics provide insights into the model’s ability to accurately classify AD cases and predict disease progression. Overall, the hybrid deep CNN with denoising using MLP and pooling layers presents a promising approach for AD prediction. The combination of these techniques enables more accurate and reliable predictions, contributing to early detection and improved patient care. The findings of this research contribute to the advancement of AD prediction methodologies and provide valuable insights for future studies in this domain.