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