A novel approach for skin lesion symmetry classification with a deep learning model. (June 2022) - Talavera-Martínez, Lidia; Bibiloni, Pedro; Giacaman, Aniza; Taberner, Rosa; Hernando, Luis Javier Del Pozo; González-Hidalgo, Manuel
Abstract: Skin cancer has become a public health problem due to its increasing incidence. However, the malignancy risk of the lesions can be reduced if diagnosed at an early stage. To do so, it is essential to identify particular characteristics such as the symmetry of lesions. In this work, we present a novel approach for skin lesion symmetry classification of dermoscopic images based on deep learning techniques. We use a CNN model, which classifies the symmetry of a skin lesion as either "fully asymmetric", "symmetric with respect to one axis", or "symmetric with respect to two axes". Moreover, we introduce a new dataset of labels for 615 skin lesions. During the experimentation framework, we also evaluate whether it is beneficial to rely on transfer learning from pre-trained CNNs or traditional learning-based methods. As a result, we present a new simple, robust and fast classification pipeline that outperforms methods based on traditional approaches or pre-trained networks, with a weighted-average F1-score of 64.5%. Highlights: We claim that the classification of skin lesion symmetry can be faced with deep learning techniques. A novel model based on deep CNNs for classifying symmetry of skin lesions is presented. An open-access dataset with symmetry labels annotated by specialists is presented. Comparisons with traditional strategies are presented and fulfill a transfer learning study.