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<p>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 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:accu{r}_{y}\)</EquationSource> </InlineEquation> of 98.75%, <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\:prec{i}_{n}\)</EquationSource> </InlineEquation> of 98.76%, <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\:sen{s}_{y}\)</EquationSource> </InlineEquation> of 98.75%, <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\:spe{c}_{y}\)</EquationSource> </InlineEquation> of 98.75%, and <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(\:{F}_{measure}\)</EquationSource> </InlineEquation> of 98.75%. The results indicate that the FISAF-KSD methodology performed better compared to existing approaches.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月12日

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  • Sahar Mansour,
  • Saad A. AlOwayyed,
  • Majdy M. Eltahir,
  • Turke Althobaiti,
  • Lama Abdulrahman Alharkan,
  • Sultan Almutairi,
  • Alanoud Subahi,
  • Mutasim Al Sadig

摘要

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 \(\:accu{r}_{y}\) of 98.75%, \(\:prec{i}_{n}\) of 98.76%, \(\:sen{s}_{y}\) of 98.75%, \(\:spe{c}_{y}\) of 98.75%, and \(\:{F}_{measure}\) of 98.75%. The results indicate that the FISAF-KSD methodology performed better compared to existing approaches.