Autor según el artículo: Banu SF; Sarker MMK; Abdel-Nasser M; Puig D; Raswan HA

Departamento: Enginyeria Informàtica i Matemàtiques

Autor/es de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Banu, Syeda Furruka / Puig Valls, Domènec Savi

Palabras clave: Pulmonary nodules Lung nodule segmentation Lung nodule detection Lung cancer Deep learning Computer-aided diagnosis Computed tomography Artificial intelligence lung nodule segmentation lung nodule detection lung cancer deep learning computer-aided diagnosis computed tomography

Resumen: Lung cancer is a deadly cancer that causes millions of deaths every year around the world. Accurate lung nodule detection and segmentation in computed tomography (CT) images is a vital step for diagnosing lung cancer early. Most existing systems face several challenges, such as the heterogeneity in CT images and variation in nodule size, shape, and location, which limit their accuracy. In an attempt to handle these challenges, this article proposes a fully automated deep learning framework that consists of lung nodule detection and segmentation models. Our proposed system comprises two cascaded stages: (1) nodule detection based on fine-tuned Faster R-CNN to localize the nodules in CT images, and (2) nodule segmentation based on the U-Net architecture with two effective blocks, namely position attention-aware weight excitation (PAWE) and channel attention-aware weight excitation (CAWE), to enhance the ability to discriminate between nodule and non-nodule feature representations. The experimental results demonstrate that the proposed system yields a Dice score of 89.79% and 90.35%, and an intersection over union (IoU) of 82.34% and 83.21% on the publicly available LUNA16 and LIDC-IDRI datasets, respectively.

Áreas temáticas: Química Process chemistry and technology Physics, applied Materials science, multidisciplinary Materials science (miscellaneous) Materials science (all) Materiais Instrumentation General materials science General engineering Fluid flow and transfer processes Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all) Engenharias ii Engenharias i Computer science applications Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência de alimentos Chemistry, multidisciplinary Biodiversidade Astronomia / física

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

Direcció de correo del autor: mohamed.abdelnasser@urv.cat domenec.puig@urv.cat syedafurruka.banu@estudiants.urv.cat

Identificador del autor: 0000-0002-1074-2441 0000-0002-0562-4205

Fecha de alta del registro: 2023-05-14

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

Enlace a la fuente original: https://www.mdpi.com/2076-3417/11/21/10132

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

Referencia al articulo segun fuente origial: Applied Sciences-Basel. 11 (21):

Referencia de l'ítem segons les normes APA: Banu SF; Sarker MMK; Abdel-Nasser M; Puig D; Raswan HA (2021). Aweu-net: An attention-aware weight excitation u-net for lung nodule segmentation. Applied Sciences-Basel, 11(21), -. DOI: 10.3390/app112110132

DOI del artículo: 10.3390/app112110132

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2021

Tipo de publicación: Journal Publications