Lightweight CNN efficiently discriminates ovarian cancer cells from a tumor microenvironment via holographic imaging flow cytometry

Holographic imaging flow cytometry (HIFC) can generate 2D quantitative phase maps of flowing cells in microchannels. When combined with convolutional neural networks (CNNs), HIFC could provide a promising stain-free approach for identifying target cells in complex cellular environments by leveraging the distinctive morphological and optical properties of different cell types. Here, we propose a lightweight CNN for HIFC image classification, tailored to distinguish ovarian cancer cells from surrounding non-neoplastic cell populations of the tumor microenvironment (TME). We show that the proposed CNN outperforms commonly used models, i.e., Resnet and VGG, with a computational cost lower than Mobilenet, the benchmark for efficiency and accuracy. Our approach could streamline ovarian cancer diagnostics and improve understanding of the TME, ultimately aiding the development of personalized treatments.