| Yazarlar |
Salomi Selvadass
|
|
P. Malin Bruntha
|
|
K. Martin Sagayam
|
Doç. Dr. Hatıra GÜNERHAN
Türkiye |
| 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 (IoU/ Jaccard Index) of 72.24%. Exploratory outcomes prove that our architecture achieves ameliorate performance. |
| Anahtar Kelimeler |
| computed tomography | computer-aided detection | dilated convolution | lung nodule segmentation | U-Net |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | Wiley |
| Dergi ISSN | 0899-9457 |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2024 |
| Cilt No | 34 |
| Sayı | 1 |
| Sayfalar | 1 / 15 |
| Doi Numarası | 10.1002/ima.22964 |
| Makale Linki | http://dx.doi.org/10.1002/ima.22964 |
| Atıf Sayıları | |
| SCOPUS | 6 |
| Google Scholar | 9 |
