Document Type
Article
Publication Date
2022
Journal Title or Book Title
Scientific Reports
Volume
12
Version
Publisher's PDF
Publisher's Statement
© The Author(s) 2022
DOI
10.1038/s41598-022-24900-4
Abstract
Accurate and reliable lung nodule segmentation in computed tomography (CT) images is required for early diagnosis of lung cancer. Some of the difficulties in detecting lung nodules include the various types and shapes of lung nodules, lung nodules near other lung structures, and similar visual aspects. This study proposes a new model named Lung_PAYNet, a pyramidal attention-based architecture, for improved lung nodule segmentation in low-dose CT images. In this architecture, the encoder and decoder are designed using an inverted residual block and swish activation function. It also employs a feature pyramid attention network between the encoder and decoder to extract exact dense features for pixel classification. The proposed architecture was compared to the existing UNet architecture, and the proposed methodology yielded significant results. The proposed model was comprehensively trained and validated using the LIDC-IDRI dataset available in the public domain. The experimental results revealed that the Lung_PAYNet delivered remarkable segmentation with a Dice similarity coefficient of 95.7%, mIOU of 91.75%, sensitivity of 92.57%, and precision of 96.75%.
Related Pillar(s)
Study
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Bruntha, P. Malin; Pandian, S. Immanuel Alex; Sagayam, K. Martin; Bandopadhyay, Shivargha; Pomplun, Marc; and Dang, Helen, "Lung_PAYNet: a pyramidal attention based deep learning network for lung nodule segmentation" (2022). Faculty Works: MCS (1984-2023). 29.
https://digitalcommons.molloy.edu/mathcomp_fac/29
