Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features. (arXiv:2201.11864v1 [eess.IV])

Web: http://arxiv.org/abs/2201.11864

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 …

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