Author, as appears in the article.: Banu SF; Sarker MMK; Abdel-Nasser M; Puig D; Raswan HA

Department: Enginyeria Informàtica i Matemàtiques

URV's Author/s: Abdelnasser Mohamed Mahmoud, Mohamed / Banu, Syeda Furruka / Puig Valls, Domènec Savi

Keywords: 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

Abstract: 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.

Thematic Areas: 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

licence for use: https://creativecommons.org/licenses/by/3.0/es/

Author's mail: mohamed.abdelnasser@urv.cat domenec.puig@urv.cat syedafurruka.banu@estudiants.urv.cat

Author identifier: 0000-0002-1074-2441 0000-0002-0562-4205

Record's date: 2023-05-14

Papper version: info:eu-repo/semantics/publishedVersion

Link to the original source: https://www.mdpi.com/2076-3417/11/21/10132

Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/

Papper original source: Applied Sciences-Basel. 11 (21):

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

Article's DOI: 10.3390/app112110132

Entity: Universitat Rovira i Virgili

Journal publication year: 2021

Publication Type: Journal Publications