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

TL;DR

This paper introduces an explainable AI model for classifying White Blood Cell Leukemia using only 24 interpretable features, achieving higher accuracy and providing insights into feature importance, which aids medical diagnosis and understanding of AI decisions.

Contribution

The paper presents a novel XAI approach that uses a small set of interpretable features, outperforming existing methods in accuracy and interpretability for WBC leukemia classification.

Findings

The model outperforms other approaches by about 4.38%.

It identifies key features influencing classification decisions.

Feature importance varies with different lab treatments.

Abstract

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.