Autor según el artículo: Singh, Vivek Kumar; Abdel-Nasser, Mohamed; Rashwan, Hatem A.; Akram, Farhan; Pandey, Nidhi; Lalande, Alain; Presles, Benoit; Romani, Santiago; Puig, Domenec;

Departamento: Enginyeria Informàtica i Matemàtiques

Autor/es de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / AKRAM, FARHAN / Puig Valls, Domènec Savi / Romaní Also, Santiago

Palabras clave: Skin lesion Residual convolution Gradient vector flow Factorized kernel Dermoscopy images Conditional generative adversarial network Channel attention

Resumen: Skin lesion segmentation in dermoscopic images is still a challenge due to the low contrast and fuzzy boundaries of lesions. Moreover, lesions have high similarity with the healthy regions in terms of appearance. In this paper, we propose an accurate skin lesion segmentation model based on a modified conditional generative adversarial network (cGAN). We introduce a new block in the encoder of cGAN called factorized channel attention (FCA), which exploits both channel attention mechanism and residual 1-D kernel factorized convolution. The channel attention mechanism increases the discriminability between the lesion and non-lesion features by taking feature channel interdependencies into account. The 1-D factorized kernel block provides extra convolutions layers with a minimum number of parameters to reduce the computations of the higher-order convolutions. Besides, we use a multi-scale input strategy to encourage the development of filters which are scale-variant (i.e., constructing a scale-invariant representation). The proposed model is assessed on three skin challenge datasets: ISBI2016, ISBI2017, and ISIC2018. It yields competitive results when compared to several state-of-the-art methods in terms of Dice coefficient and intersection over union (IoU) score. The codes of the proposed model are publicly available at https://github.com/vivek231/Skin-Project.

Áreas temáticas: Telecommunications Materials science (miscellaneous) Materials science (all) General materials science General engineering General computer science Engineering, electrical & electronic Engineering (miscellaneous) Engineering (all) Engenharias iv Engenharias iii Electrical and electronic engineering Computer science, information systems Computer science (miscellaneous) Computer science (all) Ciência da computação

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 21693536

Direcció de correo del autor: mohamed.abdelnasser@urv.cat domenec.puig@urv.cat santiago.romani@urv.cat

Identificador del autor: 0000-0002-1074-2441 0000-0002-0562-4205 0000-0001-6673-9615

Fecha de alta del registro: 2023-05-21

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

Enlace a la fuente original: https://ieeexplore.ieee.org/document/8832175

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Ieee Access. 7 130552-130565

Referencia de l'ítem segons les normes APA: Singh, Vivek Kumar; Abdel-Nasser, Mohamed; Rashwan, Hatem A.; Akram, Farhan; Pandey, Nidhi; Lalande, Alain; Presles, Benoit; Romani, Santiago; Puig, D (2019). FCA-Net: Adversarial Learning for Skin Lesion Segment Lion Based on Multi-Scale Features and Factorized Channel Attention. Ieee Access, 7(), 130552-130565. DOI: 10.1109/ACCESS.2019.2940418

DOI del artículo: 10.1109/ACCESS.2019.2940418

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2019

Tipo de publicación: Journal Publications