Avoiding adverse effects of staining reagents on cellular viability and cell signaling, label-free cell imaging and analysisis essential to personalized genomics, drug development, and cancer diagnostics. By analyzing the images of cells, imagebasedcell analytic methodologies offer a relatively simple and economical way to understand the cell heterogeneities anddevelopments. Owing to the developments in high-resolution image sensors and high-performance computation processors,the emerging lens-less digital holography techniques enable a simple and cost-effective approach to obtain label-free cellimages with large field of view and microscopic spatial resolution. In this work, the lens-less digital holography techniqueis adopted for image-based cell analysis. The holograms of three kinds of cells which are MDA-MB231, EC-109 andMCF-10A respectively were recorded by a lens-less digital holography system composed of a laser diode, a sample holder,a sensor and a laptop computer. The acquired holograms are first high-pass filtered. Then the amplitude images werereconstructed using the angular spectrum method and the sample to sensor distance was determined using the autofocusingcriteria based on the sparsity of image edges and corner points. The convolutional neural network (CNN) was used toclassify the cells. The experiments show that an accuracy of 97.2% can be achieve for two type cell classification and 91.2%for three type cell classification. It is believed that the lens-less holography combining with machine learning holds greatpromise in the application of stainless cell imaging and classification.
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