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White blood cells (WBCs) play a crucial role in safeguarding the human bodyagainst pathogens and foreign substances. Leveraging the abundance of WBCimaging data and the power of deep learning algorithms, automated WBC analysishas the potential for remarkable accuracy. However, the capability of deeplearning models to explain their WBC classification remains largely unexplored.In this study, we introduce HemaX, an explainable deep neural network-basedmodel that produces pathologist-like explanations using five attributes:granularity, cytoplasm color, nucleus shape, size relative to red blood cells,and nucleus to cytoplasm ratio (N:C), along with cell classification,localization, and segmentation. HemaX is trained and evaluated on a noveldataset, LeukoX, comprising 467 blood smear images encompassing ten (10) WBCtypes. The proposed model achieves impressive results, with an averageclassification accuracy of 81.08% and a Jaccard index of 89.16% for celllocalization. Additionally, HemaX performs well in generating the fiveexplanations with a normalized mean square error of 0.0317 for N:C ratio andover 80% accuracy for the other four attributes. Comprehensive experimentscomparing against multiple state-of-the-art models demonstrate that HemaX'sclassification accuracy remains unaffected by its ability to provideexplanations. Moreover, empirical analyses and validation by experthematologists confirm the faithfulness of explanations predicted by ourproposed model.

Pathologist-Like Explanations Unveiled: an Explainable Deep Learning System for White Blood Cell Classification