CNN–ViT Özniteliklerine Dayalı Kesit Bazlı Böbrek Organı Varlığı Sınıflandırması: Klinik Ön-Eleme Süreçlerine Yönelik Bir Yaklaşım

Year 2025, EARLY VIEW, 1 - 1

Dr. Coşku Öksüz

https://doi.org/10.2339/politeknik.1750335

Abstract

Böbrek taşı, dünya genelinde artan prevalansı ve yol açtığı komplikasyonlar nedeniyle dikkatle izlenmesi gereken önemli bir sağlık problemidir. Tanıda yaygın olarak kullanılan bilgisayarlı tomografi (BT), yüksek duyarlılık sağlasa da hacimsel veri yapısı nedeniyle radyologların inceleme süresini uzatmakta ve iş yükünü artırmaktadır. Bu çalışmada, böbrek taşı tespitine yönelik geliştirilecek karar destek sistemlerine öncülük edecek, böbrek içeren ve içermeyen BT kesitlerini ayırt edebilen, sade ve düşük maliyetli bir derin öğrenme tabanlı ön sınıflandırma modeli önerilmiştir. Modelin amacı, böbrek içermeyen kesitleri dışlayarak yalnızca ilgili görüntülerin ikinci kademe yapay zekâ modeline ve radyoloğa yönlendirilmesini sağlamaktır. Bu kapsamda, böbreğin global konumsal bağlamı için Vision Transformer (ViT), yerel öznitelikleri için ResNet18 mimarisi kullanılmış; öznitelikler kaynaştırılarak optimize edilmiş sığ bir yapay sinir ağıyla sınıflandırılmıştır. Model, gerçek hasta verileriyle geliştirilen etkileşimli bir arayüzde test edilerek pilot uygulama için entegre edilmiş ve kullanıcı senaryolarına göre değerlendirilmiştir. Önerilen model, doğru negatif sınıflamalarıyla hasta başına ortalama %64.1 oranında (~24 dakika) etiketleme süresinden tasarruf sağlamış; %89.4 doğruluk, duyarlılık ve %89.5 özgüllük ile yüksek sınıflandırma başarımı sunmuştur. Bulgular, modelin klinik entegrasyona uygun, zamandan ve işlem gücünden tasarruf sağlayan etkili bir ön sınıflandırma aracı olduğunu göstermektedir. Çalışma ayrıca, yapay zekâ sistemlerinin klinik kullanımı öncesinde pilot testlerle ve uzman katkısıyla değerlendirilmesinin önemini vurgulamaktadır.

Keywords

Böbrek tespiti , Abdominal BT , Dilim düzeyinde sınıflandırma , Derin öğrenme , Klinik karar desteği.

Project Number

123E442

Slice-Level Classification of Kidney Organ Presence Using CNN–ViT Features: Toward Clinical Pre-Screening

Abstract

Kidney stone disease is a major global health concern due to its rising prevalence and related complications. Although computed tomography (CT) is highly sensitive for diagnosis, its volumetric nature increases radiologists’ workload and review time. This study proposes a lightweight and low-cost deep learning-based pre-classification model to distinguish between CT slices containing the kidney and those that do not, as a preliminary step for kidney stone detection systems. The model aims to eliminate irrelevant slices and direct only meaningful images to both the second-stage AI model and the radiologist. Vision Transformer (ViT) was used to capture the global spatial context of the kidney, while ResNet18 extracted local features. These features were fused and classified using a shallow neural network. The model was tested within an interactive interface built using real patient data and integrated into a pilot application. Results showed that the proposed system achieved an average of 64.1% time saving per patient (~24 minutes) by filtering out non-relevant slices, with 89.4% accuracy, 89.4% recall, and 89.5% specificity. These findings suggest that the model is a practical and efficient pre-screening component for clinical workflows and highlights the importance of pilot testing and expert feedback before real-world deployment of AI systems.

Keywords

Kidney detection , Abdominal CT , Slice-level classification , Deep learning , Clinical decision support.

Ethical Statement

The abdominal CT images used in this study were retrospectively collected from the relevant medical institution with appropriate institutional permissions. All data were fully anonymized prior to processing, and no personally identifiable health information was shared or analyzed. The study protocol was approved by the Clinical Research Ethics Committee of Kastamonu University, under the approval number 2023-KAEK-160, dated 06/12/2023.

Supporting Institution

TÜBİTAK

Project Number

123E442

Thanks

This study was supported by the Scientific and Technological Research Council of Türkiye (TÜBİTAK) under project number 123E442. The author thanks TÜBİTAK for their financial support and encouragement throughout the project. The author would like to thank the Department of Radiology at Kastamonu Research and Training Hospital for providing access to the anonymized CT imaging data used in this study.

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