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Article

AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation

1
Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain
2
National Subsea Centre, Robert Gordon University, Aberdeen AB10 7GJ, UK
3
Department of Electrical Engineering, Aswan University, Aswan 81542, Egypt
*
Author to whom correspondence should be addressed.
Academic Editor: Francesco Bianconi
Appl. Sci. 2021, 11(21), 10132; https://doi.org/10.3390/app112110132
Received: 29 September 2021 / Revised: 19 October 2021 / Accepted: 22 October 2021 / Published: 28 October 2021
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
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. View Full-Text
Keywords: artificial intelligence; computer-aided diagnosis; computed tomography; lung cancer; deep learning; lung nodule detection; lung nodule segmentation artificial intelligence; computer-aided diagnosis; computed tomography; lung cancer; deep learning; lung nodule detection; lung nodule segmentation
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MDPI and ACS Style

Banu, S.F.; Sarker, M.M.K.; Abdel-Nasser, M.; Puig, D.; Raswan, H.A. AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation. Appl. Sci. 2021, 11, 10132. https://doi.org/10.3390/app112110132

AMA Style

Banu SF, Sarker MMK, Abdel-Nasser M, Puig D, Raswan HA. AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation. Applied Sciences. 2021; 11(21):10132. https://doi.org/10.3390/app112110132

Chicago/Turabian Style

Banu, Syeda F., Md. M.K. Sarker, Mohamed Abdel-Nasser, Domenec Puig, and Hatem A. Raswan 2021. "AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation" Applied Sciences 11, no. 21: 10132. https://doi.org/10.3390/app112110132

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