Kidney Stone Detection Using an EfficientNet-Based Method

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Kidney Stone Detection Using an EfficientNet-Based Method

Kidney Stone Detection Using an EfficientNet-Based Method

Pages : 1-10

View : 55 | Download : 59

Publication Date : 2025-06-01

Article Type : Research Paper

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 : Böbrek taşı tespiti, Evrişimsel sinir ağları, Sınıflandırma, Segmentasyon

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