English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Alzheimer's disease (AD) is a prevalent and debilitating neurodegenerativedisorder impacting a large aging population. Detecting AD in all itspresymptomatic and symptomatic stages is crucial for early intervention andtreatment. An active research direction is to explore machine learning methodsthat harness multimodal data fusion to outperform human inspection of medicalscans. However, existing multimodal fusion models have limitations, includingredundant computation, complex architecture, and simplistic handling of missingdata. Moreover, the preprocessing pipelines of medical scans remaininadequately detailed and are seldom optimized for individual subjects. In thispaper, we propose an efficient early-late fusion (ELF) approach, whichleverages a convolutional neural network for automated feature extraction andrandom forests for their competitive performance on small datasets.Additionally, we introduce a robust preprocessing pipeline that adapts to theunique characteristics of individual subjects and makes use of whole brainimages rather than slices or patches. Moreover, to tackle the challenge ofdetecting subtle changes in brain volume, we transform images into the Jacobiandomain (JD) to enhance both accuracy and robustness in our classification.Using MRI and CT images from the OASIS-3 dataset, our experiments demonstratethe effectiveness of the ELF approach in classifying AD into four stages withan accuracy of 97.19%.

Diagnosing Alzheimer's Disease using Early-Late Multimodal Data Fusion with Jacobian Maps