A novel ensemble learning framework based on a genetic algorithm for the classification of pneumonia
Yazarlar (2)
Doç. Dr. Mahir KAYA
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Doç. Dr. Yasemin ÇETİN KAYA
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Engineering Applications of Artificial Intelligence |
| Dergi ISSN | 1873-6769 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | İngilizce |
| Basım Tarihi | 07-2024 |
| Cilt No | 133 |
| DOI Numarası | 10.1016/j.engappai.2024.108494 |
| Makale Linki | https://www.sciencedirect.com/science/article/abs/pii/S0952197624006523 |
| Özet |
| Pneumonia is a disease that can be detected by the opacity changes in chest X-rays and can lead to fatal consequences. Medical image analysis has several challenges, such as limited labeled datasets, imbalanced class distribution, image noise, and overfitting, so individual Convolutional Neural Networks (CNNs) are insufficient to detect pneumonia accurately. Although ensemble CNN models have been used in previous studies, the literature lacks guidance on identifying the optimal CNN models and weight ratio to combine them. In this study, we propose a novel ensemble CNN framework to accurately detect pneumonia, with optimum weights set by a Genetic Algorithm (GA). Firstly, a noise outside the lung was removed, and the model performance was enhanced by performing lung segmentation on Chest X-ray. The performances of several CNN models were analyzed by hyperparameter optimization. The … |
| Anahtar Kelimeler |
| Pneumonia | Deep learning | Ensemble model | Genetic algorithm | Computer-aided diagnostics |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 14 |
| Google Scholar | 28 |