Classification Method of Corneocytes from Brilliant Green-Stained Images Using Deep Learning

Abstract

The number of parakeratotic corneocytes is an important parameter for diagnosing stratum corneum conditions. However, parakeratotic corneocytes are often visually diagnosed by an expert, which involves human error and is time-consuming. In this study, we proposed a method for classifying corneocytes, parakeratotic corneocytes, and ghost nucleus corneocytes. Our proposed system extracts each corneocyte region from a BG-stained image using a trained cell-specific deep learning model. We evaluated a method to classify corneocytes, parakeratotic corneocytes, and ghost nucleus corneocytes using different deep learning models: VGG16, VGG19, EfficientNet, EfficientNetV2, and Vision Transformer. The results showed that Vision Transformer achieved a 99.08% accuracy rate, which was sufficient for the diagnosis of stratum corneum conditions via imaging.