2026-07-21T14:14:10Zhttps://repisalud.isciii.es/rest/oai/request
oai:repisalud.isciii.es:20.500.12105/232242024-09-18T06:42:44Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16967
Repisalud author Talavera-Martínez, Lidia author Bibiloni, Pedro author González-Hidalgo, Manuel 2024-09-18T06:42:43Z 2024-09-18T06:42:43Z 2021 Talavera-Martinez L, Bibiloni P, Gonzalez-Hidalgo M. Hair Segmentation and Removal in Dermoscopic Images Using Deep Learning. IEEE Access. 2021;9:2694-704. 10.1109/ACCESS.2020.3047258 2169-3536 IEEE Access http://hdl.handle.net/20.500.13003/10553 2-s2.0-85098802256 https://hdl.handle.net/20.500.12105/23224 607730600202 Melanoma and non-melanoma skin cancers have shown a rapidly increasing incidence rate, pointing to skin cancer as a major problem for public health. When analyzing these lesions in dermoscopic images, the hairs and their shadows on the skin may occlude relevant information about the lesion at the time of diagnosis, reducing the ability of automated classification and diagnosis systems. In this work, we present a new approach for the task of hair removal on dermoscopic images based on deep learning techniques. Our proposed model relies on an encoder-decoder architecture, with convolutional neural networks, for the detection and posterior restoration of hair's pixels from the images. Moreover, we introduce a new combined loss function in the network's training phase that combines the L(1)distance, the total variation loss, and a loss function based on the structural similarity index metric. Currently, there are no datasets that contain the same images with and without hair, which is necessary to quantitatively evaluate our model. Thus, we simulate the presence of hair in hairless images extracted from publicly known datasets. We compare our results with six state-of-the-art algorithms based on traditional computer vision techniques by means of similarity measures that compare the reference hairless image and the one with simulated hair. Finally, the Wilcoxon signed-rank test is used to compare the methods. The results, both qualitatively and quantitatively, demonstrate the effectiveness of our model and how our loss function improves the restoration ability of the proposed model. eng Deep neural networks Dermoscopy Hair removal Image processing Inpainting Skin lesion Hair Segmentation and Removal in Dermoscopic Images Using Deep Learning research article