Cascaded classifiers and stacking methods for classification of pulmonary nodule characteristics
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, cilt.166, ss.77-89, 2018 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 166
- Basım Tarihi: 2018
- Doi Numarası: 10.1016/j.cmpb.2018.10.009
- Dergi Adı: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.77-89
- Hacettepe Üniversitesi Adresli: Evet
Özet
Background and Objectives: Detection and classification of pulmonary nodules are critical tasks in medical image analysis. The Lung Image Database Consortium (LIDC) database is a widely used resource for small pulmonary nodule classification research. This dataset is comprised of nodule characteristic evaluations and CT scans of patients. Although these characteristics are utilized in several studies, they can be used to improve classification performance.