An Improved V-Net Model for Thyroid Nodule Segmentation
Yazarlar (2)
Arş. Gör. Büşra YETGİNLER Kırşehir Ahi Evran Üniversitesi, Türkiye
İsmail Atacak Gazi Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Applied Sciences Switzerland (Q2) | ||
| Dergi ISSN | 2076-3417 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | |||
| Makale Dili | İngilizce | Basım Tarihi | 04-2025 |
| Cilt / Sayı / Sayfa | 15 / 7 / – | DOI | 10.3390/app15073873 |
| Makale Linki | https://www.mdpi.com/2076-3417/15/7/3873 | ||
| Özet |
| Early diagnosis of increasingly common thyroid nodules is crucial for effectively and accurately managing the disease’s monitoring and treatment process. In practice, manual segmentation methods based on ultrasound images are widely used; however, owing to the limitations arising from the imaging sources and differences based on radiologist opinions, their standalone use may not be sufficient for thyroid nodule segmentation. Therefore, there is a growing focus on developing automatic diagnostic approaches to assist radiologists in nodule diagnosis. Although current approaches have yielded successful results, more research is needed for nodule detection because of the complexity of the thyroid region, irregular tissues, and blurred boundaries. This study proposes an improved V-Net segmentation model based on fully convolutional neural networks (V-Net) and squeeze-and-excitation (SE) mechanisms for detecting thyroid nodules in two-dimensional image data. In addition to the strengths of the V-Net approach in the proposed model, a squeeze-and-excitation (SE) mechanism was used to emphasize important features and suppress irrelevant features by assigning weights to the significant features of the model. Experimental studies utilized the Digital Database Thyroid Image (DDTI) and Thyroid Nodule 3493 (TN3K) datasets, and the improved V-Net-based model was validated using the V-Net, fusion V-Net, and SEV-Net methods. The results obtained from the experimental studies demonstrate that the proposed model outperforms the V-Net, fusion V-Net, and SEV-Net models, with a Dice score of 84.51% and an IoU score of 76.27 … |
| Anahtar Kelimeler |
| deep learning | improved V-Net | medical image segmentation | thyroid nodules | ultrasound |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 10 |
| Scopus | 5 |