What question did this study set out to answer?

The aim is to develop an efficient system for accurately identifying kidney stones from CT images using deep learning techniques.

May 14, 2026Open Access

Design and validation of renal stone detection using multi-architecture feature extraction with deep sequential learning model on axial computed tomography images

Key Points

The aim is to develop an efficient system for accurately identifying kidney stones from CT images using deep learning techniques.
Developed the Feature Integration and Sequential Attention Framework for Kidney Stone Detection (FISAF-KSD).
Performed image pre-processing and augmentation to enhance CT images.
Utilized EfficientNetV2L, InceptionV3, and ResNet-101 for feature extraction, followed by a BiGRU network with attention mechanism for classification.
Achieved an accuracy of 98.75% in detecting kidney stones.
Precision was reported at 98.76%, with a sensitivity and specificity of 98.75% each.
FISAF-KSD outperformed existing approaches in kidney stone detection.

Abstract

Kidney stone disease is a significant public health threat, with its prevalence escalating due to evolving dietary habits, rising rates of obesity, other medical conditions, and the use of certain supplements. A kidney stone, otherwise known as a renal calculus, is a solid mass of crystallized minerals that aggregates within the kidneys. The proper identification of this renal condition is vital because it represents a serious health issue that requires accurate detection for effective treatment. Imaging techniques play a vital role in diagnosing kidney diseases, including kidney stones. Computed tomography (CT) is among the imaging techniques utilized to detect kidney stones by medical specialists. CT scans provide information on a stone’s specific location and size, allowing for an estimation of the chances for natural expulsion, thus potentially avoiding the need for surgical procedures. Deep learning (DL) models are progressively renowned as a robust tool for disease diagnosis in the biomedical domain. This study presents a Feature Integration and Sequential Attention Framework for Kidney Stone Detection (FISAF-KSD) approach. The primary goal of this work is to develop a reliable and efficient system that can accurately identify kidney stones from CT images. To achieve this, the FISAF-KSD approach initially performs image pre-processing and augmentation to improve input image quality and prepare CT images for further analysis. Following this, feature extraction is carried out through a fusion of three DL models, such as EfficientNetV2L, InceptionV3, and ResNet-101, to capture the key features of kidney stones at both detailed and broad levels. Finally, a bidirectional gated recurrent unit network (BiGRU) with an attention mechanism (AM) is employed to classify renal stones effectively. The performance analysis of the FISAF-KSD methodology is thoroughly examined under the Axial CT imaging dataset. The FISAF-KSD methodology accomplished \: accurₘ of 98. 75%, \: preci₍ of 98. 76%, \: sensₘ of 98. 75%, \: specₘ of 98. 75%, and \: F₌₄₀ₒₔₑ₄ of 98. 75%. The results indicate that the FISAF-KSD methodology performed better compared to existing approaches.

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