AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
| Title: | AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation |
|---|---|
| Authors: | Syeda Furruka Banu, Md. Mostafa Kamal Sarker, Mohamed Abdel-Nasser, Domenec Puig, Hatem A. Raswan |
| Source: | Applied Sciences, Vol 11, Iss 21, p 10132 (2021) |
| Publisher Information: | MDPI AG, 2021. |
| Publication Year: | 2021 |
| Collection: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| Subject Terms: | artificial intelligence, computer-aided diagnosis, computed tomography, lung cancer, deep learning, lung nodule detection, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2076-3417 |
| Relation: | https://www.mdpi.com/2076-3417/11/21/10132; https://doaj.org/toc/2076-3417 |
| DOI: | 10.3390/app112110132 |
| Access URL: | https://doaj.org/article/42f0b75d487e478abce7b0996cf5f2d4 |
| Accession Number: | edsdoj.42f0b75d487e478abce7b0996cf5f2d4 |
| Database: | Directory of Open Access Journals |
| ISSN: | 20763417 |
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| DOI: | 10.3390/app112110132 |
| Published in: | Applied Sciences |
| Language: | English |