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

Multiple modalities of biomarkers have been proved to be very sensitive inassessing the progression of Alzheimer's disease (AD), and using thesemodalities and machine learning algorithms, several approaches have beenproposed to assist in the early diagnosis of AD. Among the recent investigatedstate-of-the-art approaches, Gaussian discriminant analysis (GDA)-basedapproaches have been demonstrated to be more effective and accurate in theclassification of AD, especially for delineating its prodromal stage of mildcognitive impairment (MCI). Moreover, among those binary classificationinvestigations, the local feature extraction methods were mostly used, whichmade them hardly be applied to a practical computer aided diagnosis system.Therefore, this study presents a novel global feature extraction model takingadvantage of the recent proposed GDA-based dual high-dimensional decisionspaces, which can significantly improve the early diagnosis performancecomparing to those local feature extraction methods. In the true test using 20%held-out data, for discriminating the most challenging MCI group from thecognitively normal control (CN) group, an F1 score of 91.06%, an accuracy of88.78%, a sensitivity of 91.80%, and a specificity of 83.78% were achieved thatcan be considered as the best performance obtained so far.

A global feature extraction model for the effective computer aided diagnosis of mild cognitive impairment using structural MRI images