Deep-Learning-Based Scalp Image Analysis Using Limited Data

Abstract

The World Health Organization and Korea National Health Insurance assert that the number of alopecia patients is increasing every year, and approximately 70 percent of adults suffer from scalp problems. Although alopecia is a genetic problem, it is difficult to diagnose at an early stage. Although deep-learning-based approaches have been effective for medical image analyses, it is challenging to generate deep learning models for alopecia detection and analysis because creating an alopecia image dataset is challenging. In this paper, we present an approach for generating a model specialized for alopecia analysis that achieves high accuracy by applying data preprocessing, data augmentation, and an ensemble of deep learning models that have been effective for medical image analyses. We use an alopecia image dataset containing 526 good, 13,156 mild, 3742 moderate, and 825 severe alopecia images. The dataset was further augmented by applying normalization, geometry-based augmentation (rotate, vertical flip, horizontal flip, crop, and affine transformation), and PCA augmentation. We compare the performance of a single deep learning model using ResNet, ResNeXt, DenseNet, XceptionNet, and ensembles of these models. The best result was achieved when DenseNet, XceptionNet, and ResNet were combined to achieve an accuracy of 95.75 and an F1 score of 87.05.