| Yazarlar (6) |
Coşku Öksüz
Türkiye |
|
Artun Narter
|
Doç. Dr. Bünyamin ECE
Kastamonu Üniversitesi, Türkiye |
|
Mustafa Koyun
Türkiye |
Dr. Öğr. Üyesi İsmail TAŞKENT
Kastamonu Üniversitesi, Türkiye |
|
Mehmet Kemal Güllü
İzmir Bakırçay Üniversitesi, Türkiye |
| 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 individuals. Experiments conducted using EfficientNetV2s, MobileNetV4s, SqueezeNet, and ResNet18-based models revealed that the EfficientNetV2s and MobileNetV4s-based models excelled in terms of accuracy, while the SqueezeNet and ResNet18-based models provided stronger results in terms of interpretability. |
| Anahtar Kelimeler |
| Bildiri Türü | Tebliğ/Bildiri |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) |
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum |
| Bildiri Dili | İngilizce |
| Kongre Adı | 33rd European Signal Processing Conference EUSIPCO 2025 |
| Kongre Tarihi | 08-09-2025 / 12-09-2025 |
| Basıldığı Ülke | İtalya |
| Basıldığı Şehir | Palermo |
