EfficientNet Tabanlı Bir Yöntem Kullanılarak Böbrek Taşı Tespiti

Abstract

Bu çalışma, böbrek taşlarının doğru ve etkili teşhisi ve sınıflandırılması için derin öğrenme metodolojilerinin uygulanmasını araştırmaktadır. Çevresel ve genetik faktörlerin karmaşık etkileşimi sonucu oluşan böbrek taşları, yaşam kalitesini düşürerek ve çeşitli komplikasyon riskini artırarak insan sağlığını önemli ölçüde etkilemektedir. Manyetik rezonans görüntüleme (MRG) ve bilgisayarlı tomografi (BT) gibi görüntüleme teknikleri tanı için çok önemli olsa da, BT taramaları hastalar için radyasyon riskleri oluşturmaktadır. Bu riskleri azaltmak ve teşhis doğruluğunu artırmak için, bu araştırma derin öğrenme algoritmalarının potansiyelini araştırmaktadır. Çalışma, derin öğrenmenin gücünden yararlanarak, doğrudan BT görüntülerinden farklı böbrek taşı tiplerini doğru bir şekilde tanımlayabilen ve sınıflandırabilen sağlam bir sistem geliştirmeyi amaçlamaktadır. Bu yaklaşım, tekrarlanan BT taramalarına olan ihtiyacı en aza indirme potansiyeline sahiptir, böylece hastanın radyasyona maruz kalmasını azaltırken aynı anda teşhis hassasiyetini artırır ve potansiyel olarak daha etkili ve kişiselleştirilmiş tedavi stratejilerine yol açar.

Keywords

• Böbrek taşı tespiti
• Evrişimsel sinir ağları
• Sınıflandırma
• Segmentasyon

Kidney Stone Detection Using an EfficientNet-Based Method

Abstract

This study investigates the application of deep learning methodologies for the accurate and efficient diagnosis and classification of kidney stones. Kidney stones, resulting from a complex interplay of environmental and genetic factors, significantly impact human health by reducing quality of life and increasing the risk of various complications. While imaging techniques like magnetic resonance imaging (MRI) and computed tomography (CT) are crucial for diagnosis, CT scans pose radiation risks to patients. To mitigate these risks and improve diagnostic accuracy, this research explores the potential of deep learning algorithms. By leveraging the power of deep learning, the study aims to develop a robust system that can accurately identify and classify different types of kidney stones directly from CT images. This approach has the potential to minimize the need for repeated CT scans, thereby reducing patient exposure to radiation while simultaneously enhancing diagnostic precision and potentially leading to more effective and personalized treatment strategies.

Keywords

• Kidney stone detection
• Convolutional neural networks
• Classification
• Segmentation

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