SAtUNet: Series atrous convolution enhanced U‐Net for lung nodule segmentation
Yazarlar (4)
Salomi Selvadass
Karunya Institute Of Technology And Sciences, Hindistan
Karunya Institute Of Technology And Sciences, Hindistan
P. Malin Bruntha
Karunya Institute Of Technology And Sciences, Hindistan
Karunya Institute Of Technology And Sciences, Hindistan
K. Martin Sagayam
Karunya Institute Of Technology And Sciences, Hindistan
Karunya Institute Of Technology And Sciences, Hindistan
Doç. Dr. Hatıra GÜNERHAN Kafkas Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | International Journal of Imaging Systems and Technology (Q2) | ||
| Dergi ISSN | 0899-9457 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2024 |
| Cilt / Sayı / Sayfa | 34 / 1 / 1–15 | DOI | 10.1002/ima.22964 |
| Makale Linki | https://onlinelibrary.wiley.com/doi/full/10.1002/ima.22964 | ||
| Özet |
| Precise and unambiguous segmentation of pulmonary nodules from the CT images is imperative for a CAD framework implementation delineated for the prognosis of lung cancer. Lung nodule segmentation is an appealing research discipline for accurate dismemberment of lung cancer but the irregularity in shades, contours, and compositions, and the affinity between the tumors and the neighboring regions makes it an arduous task. This paper proffers a series atrous convolution enhanced U‐Net which uses a series of concatenated dilated convolution blocks after every stage in the encoder and decoder path. Our approach helps in obtaining the quintessential components from the feature maps, in addition to the absolute convergence of the model. It is largely assessed on the publicly accessible LIDC‐IDRI dataset. The average Dice Similarity Coefficient (DSC) obtained is 81.10% with an Intersection over Union … |
| Anahtar Kelimeler |
| computed tomography | computer-aided detection | dilated convolution | lung nodule segmentation | U-Net |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 30 |
| Web of Science | 22 |