An ensemble approach for classification of tympanic membrane conditions using soft voting classifier
Yazarlar (4)
Prof. Dr. Kemal AKYOL Kastamonu Üniversitesi, Türkiye
Emine Uçar Izmir Bakircay University, Türkiye
Doç. Dr. Ümit Atila Gazi Üniversitesi, Türkiye
Murat Uçar Izmir Bakircay University, Türkiye
| Makale Türü |
Özgün Makale
(SCOPUS dergilerinde yayınlanan tam makale)
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| Dergi Adı | Multimedia Tools and Applications | ||
| Dergi ISSN | 1380-7501 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 02-2024 |
| Kabul Tarihi | 12-02-2024 | Yayınlanma Tarihi | 22-02-2024 |
| Cilt / Sayı / Sayfa | 83 / 32 / 77809–77830 | DOI | 10.1007/s11042-024-18631-z |
| Makale Linki | https://doi.org/10.1007/s11042-024-18631-z | ||
| Özet |
| Otitis media is a medical concept that represents a range of inflammatory middle ear disorders. The high costs of medical devices utilized by field experts to diagnose the disease relevant to otitis media prevent the widespread use of these devices. This makes it difficult for field experts to make an accurate diagnosis and increases subjectivity in diagnosing the disease. To solve these problems, there is a need to develop computer-aided middle ear disease diagnosis systems. In this study, a deep learning-based approach is proposed for the detection of OM disease to meet this emerging need. This approach is the first that addresses the performance of a voting ensemble framework that uses Inception V3, DenseNet 121, VGG16, MobileNet, and EfficientNet B0 pre-trained DL models. All pre-trained CNN models used in the proposed approach were trained using the Public Ear Imagery dataset, which has a total of … |
| Anahtar Kelimeler |
| Otoscopy images | Pre-trained deep learning model | Tympanic membrane | Voting ensemble |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 14 |
| Google Scholar | 23 |