Discrimination of the Skin Cells from Cellular-Resolution Optical Coherence Tomography by Deep Learning

Optical coherence tomography (OCT) is a cellular-resolution imaging technique that can be used as non-invasive and real-time imaging and is useful for detecting early stages of diseases. Five in vitro skin cells were measured by the Mirau-based full-field OCT, including keratinocyte (HaCaT cell line), melanocyte, squamous cell carcinoma cell line (A431), and two melanoma cell lines, i.e., A375 and A2058. Deep learning algorithms (particularly convolutional neural networks, CNN) that extract features from images efficiently process the OCT’s complex images. We used four models to classify the images of five types of 2D-OCT skin cells. Based on the ResNet-15 model, the mean accuracy (average accuracy of 10-fold cross-validation) reaches 98.47%, and the standard deviation is only 0.28% with the data augmentation method. Interestingly, while two normal skin cell images mix and the other three cancer skin cell images mix, the model still works to identify normal and cancer cell features. The mean accuracy reaches 96.77%. Furthermore, we used k-fold analysis to detect the model reliability and adopt the Gradient-weighted Class Activation Mapping (GRAD-CAM) to explain the discrimination results. The deep learning algorithm is successfully and efficiently applied to discriminate the OCT skin cell images.