A Deep Learning Approach for Automated Detection and Classification of Alzheimer’s Disease

Abstract

Alzheimer’s Disease is progressive dementia that begins with minor memory loss and develops into the complete loss of mental and physical abilities. Memory-related regions of the brain, such as the entorhinal cortex and hippocampus, are the first to be damaged. A person’s mental stability is harmed as a result of the severity. Later on, it affects cortical regions that engage with language, logic, and social interaction. Subsequently, it spreads to other parts of the brain, resulting in a substantial reduction in brain volume. Although computer-aided algorithms have achieved significant advances in research, there is still room for improvement in the feasible diagnostic procedure accessible in clinical practice. Deep learning models have gone mainstream in the latest decades due to their superior performance. Compared to typical machine learning approaches, deep models are more accurate in detecting Alzheimer’s Disease. To identify the labels as demented or non-demented, the researchers used the Open Access Series of Imaging Studies (OASIS) dataset. The novelty comes in doing extensive research to uncover crucial predictor factors and then selecting a Deep Neural Network (DNN) with five hidden layers and carefully tuned hyper-parameters to achieve exemplary performance. The assertions were supported by evidence of 90.10% correlation accuracy at various iterations and layers.