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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Since microaneurysms (MAs) can be seen as the earliest lesions in diabetic retinopathy, its detection plays a critical role in the diabetic retinopathy diagnosis. In recent years, many machine-learning methods have been developed for MA detection. Generally, MA candidates are first identified and then a set of features for these candidates are extracted. Finally, machine-learning methods are applied for candidate classification. In this paper, we present a novel unsupervised classification method based on sparse posterior cerebral artery (PCA) for MA detection. Since it does not have to consider a non-MA training set, the class imbalance problem can be avoided. Furthermore, effective features can be selected due to the characteristic of sparse PCA, which combines the elastic net penalty with the PCA. Meanwhile, a single T<sup>2</sup> statistic is introduced, and the control limit can be determined for distinguishing true MAs from spurious candidates automatically. Experiment results on the retinopathy online challenge competition database show the effectiveness of our proposed method.

Automatic Microaneurysm Detection Using the Sparse Principal Component Analysis-Based Unsupervised Classification Method