ADEMNET architecture: An innovative solution for adaptive multi-class balancing problem in image classification

Abstract

In the field of medical image processing, achieving high performance in the classification of four types of dementia poses a significant challenge. This research presents a novel approach that outperforms existing methodologies, bringing about a transformative impact in this specialized domain. The method integrates the adaptive synthetic–nominal (ADASYN) technique with a DEMNET framework, resulting in a substantial performance improvement of 95.45% compared to current benchmarks. Through meticulous experimentation on a dementia dataset encompassing four distinct types, we consistently demonstrate significant enhancements achieved by the refined strategy. This innovation not only raises the performance standard but also provides a robust and adaptable solution that can be easily integrated into existing systems. The implications of this advancement open up new avenues for both research and practical applications. This work exemplifies the power of innovative approaches to push the limits of performance and establishes a new benchmark for excellence within this specific domain.