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

Although cervical cancer is fully curable if diagnosis is early achieved, it is still the most frequent female fatal disease worldwide, the second death cause in female population. On the other hand, Papanicolau smear test is the screening method for detecting abnormalities in the uterine cervix cells, including the precancerous cells. Provided that this test is extremely labor intensive and reader dependant, automated screening has been pursued during last decades, resulting in some commercial developments that identify suspicious regions i.e. regions with the possibility of containing malignant cells, but without an accurate identification of pathological cells. As a consequence, cell classification is still an open research problem, usually approached by characterizing morphological features that are highly dependent on the accurate segmentation of the boundaries. In this paper we propose a cell classification method that instead of attempting to segment the cell cytoplasm and nucleus, it characterizes the very inner cell relationships using global features and a standard classifier, the k nearest neighbour, that learns a particular data partition.

Pap smear cell image classification using global MPEG-7 descriptors