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Each cell is rolled during flow, and its off\u2010axis holograms from multiple viewpoints are acquired. Using the reconstructed quantitative phase profiles of the cell projections, highly discriminating features, enabling cell detection, classification, and differentiation, are extracted via a modified ResNet\u201018 deep convolutional neural network architecture. The model is first validated by classifying metastatic breast carcinoma cells (MCF\u20107) and normal human mammary epithelial cells (MCF\u201010A). An increase in classification accuracy by 1% is achieved when processing five interferometric projections versus processing a single interferometric projection. This model is further tested on four types of white blood cells and exhibits an accuracy increase of 5% when processing 12 interferometric projections versus processing a single interferometric projection. 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