Abstract

This research paper introduces a novel approach for the classification of dementia disease using Rotation Forests based on Deep Convolutional Generative Adversarial Networks (DCGAN). Dementia is a significant cognitive disorder prevalent among the elderly population, demanding accurate and early diagnosis for effective intervention. Traditional methods often rely on manual feature extraction and shallow learning, which may lack the ability to capture intricate patterns in complex medical data. In this study, we propose a fusion of Rotation Forests, a robust ensemble learning technique, with DCGAN, a deep learning model recognized for its feature extraction capabilities. The Rotation Forests enhance the diversity of the base classifiers, while DCGAN learns meaningful features from raw medical imaging data. We validate the proposed approach on a comprehensive dataset and compare its performance against existing methods. The experimental results demonstrate the effectiveness of the Rotation Forests based on DCGAN approach in accurately classifying dementia diseases, showcasing its potential as a valuable tool in medical diagnosis.