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

White Blood Cell (WBC) Leukaemia is detected through image-basedclassification. Convolutional Neural Networks are used to learn the featuresneeded to classify images of cells a malignant or healthy. However, this typeof model requires learning a large number of parameters and is difficult tointerpret and explain. Explainable AI (XAI) attempts to alleviate this issue byproviding insights to how models make decisions. Therefore, we present an XAImodel which uses only 24 explainable and interpretable features and is highlycompetitive to other approaches by outperforming them by about 4.38\%. Further,our approach provides insight into which variables are the most important forthe classification of the cells. This insight provides evidence that when labstreat the WBCs differently, the importance of various metrics changessubstantially. Understanding the important features for classification is vitalin medical imaging diagnosis and, by extension, understanding the AI modelsbuilt in scientific pursuits.

Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features