Optimization of Deep Learning Based Segmentation Method
Yazarlar (2)
Doç. Dr. Özkan İNİK
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Konya Teknik Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | SOFT COMPUTING |
| Dergi ISSN | 1432-7643 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 04-2022 |
| Cilt No | 26 |
| Sayı | 7 |
| Sayfalar | 3329 / 3344 |
| DOI Numarası | 10.1007/s00500-021-06711-3 |
| Makale Linki | https://doi.org/10.1007/s00500-021-06711-3 |
| Özet |
| The use of deep learning models has become widespread in different computer vision problems such as classification, detection, and segmentation. Many deep learning models have been developed in the segmentation of medical images. Although segmentation accuracy has been increased, segmentation performance needs to be improved due to the variability of tissue, cell and image acquisition methods. In the deep-learning-based segmentation and classification methods, the parameters of the method should be optimized in order to obtain more successful results for segmentation. In this study, the optimization of the parameters has been performed with five optimization algorithms according to segmentation loss. These algorithms are Grey Wolf Optimizer, Artificial Bee Colony (ABC), Genetic Algorithm, Particle Swarm Optimization (PSO), and Black Widow Optimization (BWO). In the experimental studies … |
| Anahtar Kelimeler |
| Artificial bee colony (ABC) | Black widow optimization (BWO) | CNN | Deep learning | Genetic algorithm (GA) | Grey wolf optimizer (GWO) | Parameter optimization | Particle swarm optimization (PSO) | Segmentation |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 6 |
| Google Scholar | 10 |