2026-07-21T11:28:29Zhttps://repisalud.isciii.es/rest/oai/request
oai:repisalud.isciii.es:20.500.12105/233672024-11-29T06:34:30Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16967
Repisalud author Talavera-Martínez, Lidia author Bibiloni, Pedro author Giacaman, Aniza author Taberner, Rosa author del Pozo Hernando, Luis Javier author González-Hidalgo, Manuel 2024-10-04T13:16:20Z 2024-10-04T13:16:20Z 2022 Talavera-Martínez L, Bibiloni P, Giacaman A, Taberner R, Hernando LJDP, González-Hidalgo M. A novel approach for skin lesion symmetry classification with a deep learning model. Comput Biol Med. 2022;145:105450. 10.1016/j.compbiomed.2022.105450 1879-0534 Computers in biology and medicine http://hdl.handle.net/20.500.13003/18065 35364312 L2017484307 2-s2.0-85127098123 https://hdl.handle.net/20.500.12105/23367 819697000002 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%. eng A novel approach for skin lesion symmetry classification with a deep learning model. research article