Automated Recognition of Alzheimer's Dementia Using Bag-of-Deep-Features and Model Ensembling

Abstract

Alzheimer's dementia is a progressive neurodegenerative disease that causes cognitive and physical impairment. It severely deteriorates the quality of life in affected individuals. An early diagnosis can assist immensely in better management of their healthcare needs. In recent years, there has been a renewed impetus in development of automated methods for recognition of various disorders by leveraging advancements in artificial intelligence. Here, we propose a multimodal system that can identify linguistic and paralinguistic traits of dementia using an automated screening tool. We show that bag-of-deep-neural-embeddings and ensemble learning offer a viable approach to objective assessment of dementia. The developed system is tested on the Alzheimer's Dementia Recognition Challenge dataset, where it achieved a new state-of-the-art (SOTA) performance for the classification task and matched the current SOTA for the regression task. These results highlight the efficacy of our proposed system for facilitating an early diagnosis of dementia.