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insight - White blood cell classification
Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images
A novel approach based on neural cellular automata (NCA) for white blood cell classification that achieves competitive performance, is significantly smaller in terms of parameters, exhibits robustness to domain shifts, and provides inherent explainability.
Automated Classification of Four Major White Blood Cell Types Using Convolutional Neural Networks
A convolutional neural network (CNN) model is proposed to accurately classify the four major subtypes of white blood cells (WBCs) - eosinophils, lymphocytes, monocytes, and neutrophils - with high performance.