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

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

doi:10.1038/s41598-026-45383-7

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

Sci Rep. 2026 May 12. doi: 10.1038/s41598-026-45383-7. Online ahead of print.

Authors

Sahar Mansour¹, Saad A AlOwayyed², Majdy M Eltahir³, Turke Althobaiti⁴, Lama Abdulrahman Alharkan⁵, Sultan Almutairi⁶, Alanoud Subahi⁷, Mutasim Al Sadig⁸

Affiliations

¹ Department of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
² Quantum Technology and Advanced Computing Institute, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia.
³ Department of Information Systems, Applied College at Mahayil, King Khalid University, Abha, Saudi Arabia.
⁴ Department of Computer Science, Faculty of Science, Northern Border University, Arar, 73222, Saudi Arabia.
⁵ Department of Design, College of Arts, King Saud University, P.O. Box 11451, Riyadh, Saudi Arabia.
⁶ Department of Computer Science, Applied College, Shaqra University, Shaqra, 15526, Saudi Arabia. sultan@su.edu.sa.
⁷ Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh, 25732, Saudi Arabia.
⁸ Department of Computer Science, College of Science, Majmaah University, Al Majmaah, 11952, Saudi Arabia.

PMID: 42120894
DOI: 10.1038/s41598-026-45383-7

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 [Formula: see text] of 98.75%, [Formula: see text] of 98.76%, [Formula: see text] of 98.75%, [Formula: see text] of 98.75%, and [Formula: see text] of 98.75%. The results indicate that the FISAF-KSD methodology performed better compared to existing approaches.

Keywords: Bidirectional Gated Recurrent Unit; Computed Tomography; Deep Learning; InceptionV3; Kidney Stone; Medical Images.