Abc-based weighted voting deep ensemble learning model for multiple eye disease detection
Yazarlar (3)
Dr. Öğr. Üyesi Kübra Uyar Alanya Alaaddin Keykubat University, Türkiye
Arş. Gör. Mustafa Yurdakul Kirikkale Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Sinop Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Biomedical Signal Processing and Control (Q1) | ||
| Dergi ISSN | 1746-8094 Dergi Bilgileri (2024) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 07-2024 |
| Cilt / Sayı / Sayfa | 96 / 1 / 106617–0 | DOI | 10.1016/j.bspc.2024.106617 |
| Makale Linki | https://doi.org/10.1016/j.bspc.2024.106617 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| Background and objectiveThe unique organ that provides vision is eye and there are various disorders cause visual impairment. Therefore, the identification of eye diseases in early period is significant to take necessary precautions. Convolutional Neural Network (CNN), successfully used in various imageanalysis problems due to its automatic data-dependent feature learning ability, can be employed with ensemble learning.MethodsA novel approach that combines CNNs with the robustness of ensemble learning to classify eye diseases was designed. From a comprehensive evaluation of fifteen pre-trained CNN models on the Eye Disease Dataset (EDD), three models that exhibited the best classification performance were identified. Instead of employing traditional ensemble methods, these CNN models were integrated using a weighted-voting mechanism, where the contribution of each model was determined … |
| Anahtar Kelimeler |
| Artificial bee colony | CNN | Ensemble learning | Eye disease classification | Weighted voting |
Science Direct
Abc-based weighted voting deep ensemble learning model for multiple eye disease detection
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 27 |
| Scopus | 32 |
| Google Scholar | 45 |