Autor segons l'article: Sarker, Md Mostafa Kamal; Rashwan, Hatem A; Akram, Farhan; Singh, Vivek Kumar; Banu, Syeda Furruka; Chowdhury, Forhad U H; Choudhury, Kabir Ahmed; Chambon, Sylvie; Radeva, Petia; Puig, Domenec; Abdel-Nasser, Mohamed
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Abdelnasser Mohamed Mahmoud, Mohamed / AKRAM, FARHAN / Banu, Syeda Furruka / Puig Valls, Domènec Savi
Paraules clau: Textures Skin lesion segmentation Skin lesion Segmentation algorithms Position attention Lesion segmentations Image segmentation Diagnosis Dermoscopic images Dermatology Deep learning Deep generative adversarial network Deep Classification Channel attention Adversarial networks 1-d kernel factorized network
Resum: The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the color, texture and shapes of skin lesions. Existing deep learning-based skin lesion segmentation algorithms are expensive in terms of computational time and memory. Consequently, running such segmentation algorithms requires a powerful GPU and high bandwidth memory, which are not available in dermoscopy devices. Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model. The 1-D kernel factorized network reduces the computational cost of 2D filtering. The position and channel attention modules enhance the discriminative ability between the lesion and non-lesion feature representations in spatial and channel dimensions, respectively. A multiscale block is also used to aggregate the coarse-to-fine features of input skin images and reduce the effect of the artifacts. SLSNet is evaluated on two publicly available datasets: ISBI 2017 and the ISIC 2018. Although SLSNet has only 2.35 million parameters, the experimental results demonstrate that it achieves segmentation results on a par with the state-of-the-art skin lesion segmentation methods with an accuracy of 97.61%, and Dice and Jaccard similarity coefficients of 90.63% and 81.98%, respectively. SLSNet can run at more than 110 frames per second (FPS) in a single GTX1080Ti GPU, which is faster than well-known deep learning-based image segmentation models, such as FCN. Therefore, SLSNet can be used for practical dermoscopic applications.
Àrees temàtiques: Química Operations research & management science Medicina iii Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Interdisciplinar Geociências General engineering Farmacia Engineering, electrical & electronic Engineering (miscellaneous) Engineering (all) Engenharias iv Engenharias iii Engenharias ii Engenharias i Enfermagem Educação Economia Direito Computer science, artificial intelligence Computer science applications Ciências sociais aplicadas i Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência da computação Biotecnología Biodiversidade Astronomia / física Artificial intelligence Arquitetura, urbanismo e design Administração, ciências contábeis e turismo Administração pública e de empresas, ciências contábeis e turismo
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: mohamed.abdelnasser@urv.cat hatem.abdellatif@urv.cat syedafurruka.banu@estudiants.urv.cat domenec.puig@urv.cat
Identificador de l'autor: 0000-0002-1074-2441 0000-0001-5421-1637 0000-0002-5624-1941 0000-0002-0562-4205
Data d'alta del registre: 2024-09-21
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.sciencedirect.com/science/article/pii/S0957417421008496
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Expert Systems With Applications. 183 115433-
Referència de l'ítem segons les normes APA: Sarker, Md Mostafa Kamal; Rashwan, Hatem A; Akram, Farhan; Singh, Vivek Kumar; Banu, Syeda Furruka; Chowdhury, Forhad U H; Choudhury, Kabir Ahmed; Cha (2021). SLSNet: Skin lesion segmentation using a lightweight generative adversarial network. Expert Systems With Applications, 183(), 115433-. DOI: 10.1016/j.eswa.2021.115433
DOI de l'article: 10.1016/j.eswa.2021.115433
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2021
Tipus de publicació: Journal Publications
Repositori URV