A Hybrid CNN Framework for Kidney Stone Detection Using Transfer Learning and Feature Fusion
Yazarlar (6)
Coşku Öksüz Izmir Bakircay University, Türkiye
Artun Narter
Izmir Bakircay University, Türkiye
Izmir Bakircay University, Türkiye
Doç. Dr. Bünyamin ECE Kastamonu Üniversitesi, Türkiye
Prof. Dr. Mustafa Koyun Kastamonu Üniversitesi, Türkiye
Doç. Dr. İsmail TAŞKENT Kastamonu Üniversitesi, Türkiye
M. Kemal Güllü
Izmir Bakircay University, Türkiye
Izmir Bakircay University, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.23919/EUSIPCO63237.2025.11226820 | ||
| Kongre Adı | 33rd European Signal Processing Conference EUSIPCO 2025 | ||
| Kongre Tarihi | 08-09-2025 / 12-09-2025 | ||
| Basıldığı Ülke | İtalya | Basıldığı Şehir | Palermo |
| Bildiri Linki | https://doi.org/10.23919/eusipco63237.2025.11226820 | ||
| UAK Araştırma Alanları |
Radyoloji
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| Özet |
| In this study, a deep learning method for kidney stone detection is proposed. The method utilizes transfer learning by extracting features from a pre-trained ImageNet model. However, unlike traditional transfer learning, which directly applies or fine-tunes a pre-trained model, the proposed approach integrates a custom-designed CNN that operates in parallel with the pre-trained network. The feature maps obtained from both networks are fused to enhance the model's representation power. After this integration, task-specific classification layers are added, and the training process is conducted on both the classification layers and the optimized model. This approach improves the overall performance of the model while providing a more efficient training process. As part of this study, a new dataset was created, consisting of 2166 axial slice images from 241 patients and 2018 axial slice images from 46 healthy … |
| Anahtar Kelimeler |
| classification | deep learning | detection | Kidney stone | transfer learning |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 3 |
| Google Scholar | 3 |