Alzheimer's detection by Artificial Bee Colony and Convolutional Neural Network at Mobile Environment

Abstract

Alzheimer's disease (AD) presents a significant challenge in healthcare, particularly in its early detection. In this paper, we will introduce an innovative methodology that leverages the synergies of the Artificial Bee Colony (ABC) algorithm and Convolutional Neural Network (CNN) within a mobile environment to enhance the detection and diagnosis of Alzheimer's. The proposed system architecture integrates the ABC algorithm for feature optimization and CNN for image classification, specifically designed for mobile platforms. Our methodology emphasizes the efficient and accurate analysis of brain scans, specifically tailored to tackle the computational constraints inherent in mobile devices. These findings indicate that the integration of ABC and CNN within a mobile context could serve as a viable solution for early and accessible detection of Alzheimer's, potentially facilitating timely intervention and improving patient outcomes.