In the tumor microenvironment, cell interactions play a crucial role in influencing the morphology and metastasis characteristics of non-cancerous and cancerous cells. Although machine learning techniques have been proven effective in classifying individually cultured cell lines, their accuracy may deteriorate in classifying a co-cultured mixture of cells. In the proposed work, the optical path difference images of human dermal fibroblast and melanoma A375 cells were recorded, individually and in a mixture, using the off-axis digital holographic microscopy setup. A dataset consisted of segmented and labeled images of both sample types. The dataset was used to train a convolutional neural network through transfer learning to extract the morphology relevant features. An XGBoost classifier trained on the extracted features is found to effectively recognize morphological changes in cells within a mixture and classify them accurately with a limited size dataset.
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