Feature fusion-based ensemble CNN learning optimization for automated detection of pediatric pneumonia
Yazarlar (1)
Doç. Dr. Mahir KAYA
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Biomedical Signal Processing and Control |
| Dergi ISSN | 1746-8094 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | Türkçe |
| Basım Tarihi | 01-2024 |
| Cilt No | 87 |
| Sayı | 1 |
| DOI Numarası | 10.1016/j.bspc.2023.105472 |
| Makale Linki | http://dx.doi.org/10.1016/j.bspc.2023.105472 |
| Özet |
| Detecting pediatric pneumonia accurately and rapidly is crucial for timely treatment, especially considering its association with seasonal changes and potentially fatal outcomes. However, medical image analysis using convolutional neural network (CNN) models faces challenges such as limited labeled data, image noise, class imbalance, and overfitting. Regularization techniques are often insufficient, necessitating advanced approaches for successful pneumonia detection. Our study aims to accurately detect pneumonia by proposing an ensemble CNN framework that incorporates optimal feature fusion. A novel image preprocessing algorithm has been developed that applies hierarchical template-matching to reduce image noise and improves the learning of relevant features. Instead of relying solely on a few pre-defined CNN models combined through majority voting, multiple CNN models with different … |
| Anahtar Kelimeler |
| Pneumonia | CNN | Ensemble model | Feature fusion | Computer-aided diagnostics |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 23 |
| Google Scholar | 51 |