Deep Sparse Bayesian Neural Networks for Early Detection of Cognitive Impairment in Alzheimer’s Disease

Abstract

Early diagnosis of cognitive impairment in Alzheimer’s disease (AD) is
important for improving disease management, allowing for earlier
intervention and improving patient outcomes and quality of life. When
screening tools are adequately constructed, clinicians can implement
treatment plans and supportive measures that are tailored to the specific
needs of each patient. AD is a major health concern that demands early
diagnostic approaches. We propose a new approach combining deep
learning and Bayesian theory, known as Deep Sparse Bayesian Neural
Networks (DSBNN). Current approaches suffer from high data complexity
and low sensitivity to subtle cognitive changes, rendering them unreliable
for early diagnosis. There are also concerns about their interpretability and
generalisability to different patient populations. The DSBNN system
integrates sparsity-inducing priors into deep learning architectures to
increase interpretability and enable automatic feature selection and model
uncertainty assessment. It enables greater sensitivity to small changes in
cognition. DSBNN is also useful for addressing generalisation issues,
combining several data sources and transparent decision-making to enable
clinicians to leverage deep learning. This enables the earlier, more accurate
and reliable detection of cognitive decline in Alzheimer's disease, with
results that surpass those of traditional approaches, demonstrating the
significance of DSBNN in early detection. The proposed DSBNN successfully
combines the advantages of deep learning and Bayesian inference to
provide a realistic way to improve the detection and management of
Alzheimer’s disease