Automated Stain-Free Holographic Image-Based Phenotypic Classification of Elliptical Cancer Cells

Abstract

Image-based stain-free elliptical cancer cell classification is very challenging due to interclass morphological similarity. Herein, the classification of three types of cancer cell lines (lung, breast, and skin) by feature-based machine learning and image-based deep learning with a convolutional neural network (CNN) is addressed. Digital holography in a microscopic configuration is used to obtain stain-free quantitative phase images representing the intracellular content and morphology of cells. In feature-based classification, several features related to both the intracellular material and thickness of cancer cells are extracted, followed by the feature selection and the training of random forest, support vector machine, and pattern recognition artificial neural networks. For image-based classification, two types of deep learning CNN models are trained: skip connections (Resnet) and without the skip connection. The accuracy of the two strategies is analyzed and the deep learning strategy outperforms feature-based classification by about 9% with the 10-fold cross-validation evaluation.