Automating Kidney Disease Diagnosis: A Segmentation-Based, Dual-Input Approach for Classifying Acute and Chronic Kidney Disease from Ultrasound Images
Received: 15 November 2025 | Revised: 18 December 2025, 27 December 2025, 31 December 2025, 4 January 2026, and 11 January 2026 | Accepted: 6 January 2026 | Online: 9 February 2026
Corresponding author: Nora Alkhaldi
Abstract
Chronic Kidney Disease (CKD) and Acute Kidney Injury (AKI) are serious health conditions, where ultrasound imaging serves as a non-invasive diagnostic tool. Diagnosis relies on identifying significant morphological features, such as a shrunken appearance in CKD or an enlarged, echogenic kidney in AKI. This study proposes and evaluates a two-stage deep learning framework developed using a clinical dataset collected from the Saudi Ministry of National Guard Health Affairs (NGHA). The process utilizes a modified U-Net model for precise kidney segmentation, followed by a novel dual-input classification stage that integrates both the original and segmented images to enhance feature extraction. A comprehensive analysis was conducted on four advanced architectures, including a Swin Transformer, DenseNet121, EfficientNetB0, and ResNet50. The results demonstrated that the ResNet50 model with dual-input configuration, preceded by SS-MUNet segmentation, was highly effective, achieving a test accuracy of 82.88% and an F1-score of 78.36%. Dual-input setups consistently outperformed single-input variants by 10-15% in F1-score across architectures, with ResNet50 and DenseNet121 showing the strongest generalization. This approach enhances diagnostic performance by using both segmented and non-segmented images to distinguish pathological kidney features with high accuracy. The findings establish a robust framework that holds considerable potential as a speedy, reliable, and accessible decision-support tool for clinical practice in nephrology. The proposed dual-input framework based on both original and segmented kidney images improves macro F1-score by an average of 12.5% in the range of 10.2–15.3% and accuracy by 9–13% compared to single-input baselines across four modern architectures on the independent test set. This clinically significant gain enables more reliable automated differentiation of AKI, CKD, and normal kidneys using only standard ultrasound.
Keywords:
medical image analysis, acute kidney injury, chronic kidney disease, segmentation, classification, transformerDownloads
References
R. A. Alassaf et al., "Preemptive Diagnosis of Chronic Kidney Disease Using Machine Learning Techniques," in 2018 International Conference on Innovations in Information Technology (IIT), Al Ain, Nov. 2018, pp. 99–104. DOI: https://doi.org/10.1109/INNOVATIONS.2018.8606040
C. Ronco, R. Bellomo, and J. A. Kellum, "Acute Kidney Injury," The Lancet, vol. 394, no. 10212, pp. 1949–1964, Nov. 2019. DOI: https://doi.org/10.1016/S0140-6736(19)32563-2
K. Hansen, M. Nielsen, and C. Ewertsen, "Ultrasonography of the Kidney: A Pictorial Review," Diagnostics, vol. 6, no. 1, Dec. 2015, Art. no. 2. DOI: https://doi.org/10.3390/diagnostics6010002
"Kidney Diseases," Saudi Arabia Ministry of Health, 2025. https://www.moh.gov.sa/en/HealthAwareness/EducationalContent/Diseases/Urology/Pages/002.aspx.
J.-W. Hsieh, C.-H. Lee, Y.-C. Chen, W.-S. Lee, and H.-F. Chiang, "Stage Classification in Chronic Kidney Disease by Ultrasound Image," in Proceedings of the 29th International Conference on Image and Vision Computing New Zealand, Hamilton, New Zealand, Nov. 2014, pp. 271–276. DOI: https://doi.org/10.1145/2683405.2683457
A. Anggrawan, K. Hidjah, and Q. S. Jihadil, "Kidney Failure Diagnosis based on Case-Based Reasoning (CBR) Method and Statistical Analysis," in 2016 International Conference on Informatics and Computing (ICIC), Mataram, Indonesia, 2016, pp. 298–303. DOI: https://doi.org/10.1109/IAC.2016.7905733
S. Gopika and M. Vanitha, "Survey on Prediction of Kidney Disease by using Data Mining Techniques," IJARCCE, pp. 198–201, Mar. 2017. DOI: https://doi.org/10.17148/IJARCCE.2017.6138
P. Yildirim, "Chronic Kidney Disease Prediction on Imbalanced Data by Multilayer Perceptron," in 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), Turin, July 2017, pp. 193–198. DOI: https://doi.org/10.1109/COMPSAC.2017.84
M. Udhayarasu, K. Ramakrishnan, and S. Periasamy, "Assessment of Chronic Kidney Disease using Skin Texture as a Key Parameter: For South Indian Population," Healthcare Technology Letters, vol. 4, no. 6, pp. 223–227, Dec. 2017. DOI: https://doi.org/10.1049/htl.2016.0098
Y. He, X. Yu, C. Liu, J. Zhang, K. Hu, and H. C. Zhu, "A 3D Dual Path U-Net of Cancer Segmentation Based on MRI," in 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), Chongqing, June 2018, pp. 268–272. DOI: https://doi.org/10.1109/ICIVC.2018.8492781
A. Sobrinho, A. C. M. D. S. Queiroz, L. Dias Da Silva, E. De Barros Costa, M. Eliete Pinheiro, and A. Perkusich, "Computer-Aided Diagnosis of Chronic Kidney Disease in Developing Countries: A Comparative Analysis of Machine Learning Techniques," IEEE Access, vol. 8, pp. 25407–25419, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.2971208
C. Sabanayagam et al., "A Deep Learning Algorithm to Detect Chronic Kidney Disease from Retinal Photographs in Community-Based Populations," The Lancet Digital Health, vol. 2, no. 6, pp. e295–e302, June 2020. DOI: https://doi.org/10.1016/S2589-7500(20)30063-7
K. Chaudhary et al., "Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury," Clinical Journal of the American Society of Nephrology, vol. 15, no. 11, pp. 1557–1565, Nov. 2020. DOI: https://doi.org/10.2215/CJN.09330819
N. Rank et al., "Deep-Learning-Based Real-Time Prediction of Acute Kidney Injury Outperforms Human Predictive Performance," npj Digital Medicine, vol. 3, no. 1, Oct. 2020, Art. no. 139. DOI: https://doi.org/10.1038/s41746-020-00346-8
O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation," in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, vol. 9351, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, Eds. Cham: Springer International Publishing, 2015, pp. 234–241. DOI: https://doi.org/10.1007/978-3-319-24574-4_28
K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, June 2016, pp. 770–778. DOI: https://doi.org/10.1109/CVPR.2016.90
O. Oktay et al., "Attention U-Net: Learning Where to Look for the Pancreas." arXiv, 2018.
V. Alevizos and M. Hon, "Comparison of Kidney Segmentation Under Attention U-Net Architectures." Nov. 01, 2021. DOI: https://doi.org/10.36227/techrxiv.16546281.v2
L. Li, M. Verma, Y. Nakashima, H. Nagahara, and R. Kawasaki, "IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks," in 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass Village, CO, USA, Mar. 2020, pp. 3645–3654. DOI: https://doi.org/10.1109/WACV45572.2020.9093621
Y. Luo, J. Liu, and M. Ding, "CC-Net: Consistency Learning Combined with Contrastive Learning for Kidney Ultrasound Segmentation," in 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Kuching, Malaysia, Oct. 2024, pp. 821–826. DOI: https://doi.org/10.1109/SMC54092.2024.10830962
R. Khan et al., "MLAU-Net: Deep Supervised Attention and Hybrid Loss Strategies for Enhanced Segmentation of Low-Resolution Kidney Ultrasound," Digital Health, vol. 10, Jan. 2024, Art. no. 20552076241291306. DOI: https://doi.org/10.1177/20552076241291306
B. Lei et al., "Skin Lesion Segmentation Via Generative Adversarial Networks with Dual Discriminators," Medical Image Analysis, vol. 64, Aug. 2020, Art. no. 101716. DOI: https://doi.org/10.1016/j.media.2020.101716
Y.-C. Chang, C.-M. Lo, Y.-K. Chen, P.-H. Wu, and H. Luh, "W-Net: Two-Stage Segmentation for Multi-Center Kidney Ultrasound," in 2024 IEEE Conference on Artificial Intelligence (CAI), Singapore, Singapore, June 2024, pp. 1522–1523. DOI: https://doi.org/10.1109/CAI59869.2024.00274
G.-P. Chen et al., "Asymmetric U-Shaped Network with Hybrid Attention Mechanism for Kidney Ultrasound Images Segmentation," Expert Systems with Applications, vol. 212, Feb. 2023, Art. no. 118847. DOI: https://doi.org/10.1016/j.eswa.2022.118847
S. L. P. Sitanaboina, S. R. Beeram, H. Jonnadula, and L. Paleti, "Attention 3D-CU-Net: Enhancing Kidney Tumor Segmentation Accuracy Through Selective Feature Emphasis," IEEE Access, vol. 11, pp. 139798–139810, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3340912
M. G. Oghli et al., "Fully automated kidney image biomarker prediction in ultrasound scans using Fast-Unet++," Scientific Reports, vol. 14, no. 1, Feb. 2024, Art. no. 4782. DOI: https://doi.org/10.1038/s41598-024-55106-5
M. Krithika Alias AnbuDevi and K. Suganthi, "Review of Semantic Segmentation of Medical Images Using Modified Architectures of UNET," Diagnostics, vol. 12, no. 12, Dec. 2022, Art. no. 3064. DOI: https://doi.org/10.3390/diagnostics12123064
C.-C. Kuo et al., "Automation of the Kidney Function Prediction and Classification Through Ultrasound-Based Kidney Imaging using Deep Learning," npj Digital Medicine, vol. 2, no. 1, Apr. 2019, Art. no. 29. DOI: https://doi.org/10.1038/s41746-019-0104-2
F. Ma, T. Sun, L. Liu, and H. Jing, "Detection and Diagnosis of Chronic Kidney Disease Using Deep Learning-Based Heterogeneous Modified Artificial Neural Network," Future Generation Computer Systems, vol. 111, pp. 17–26, Oct. 2020. DOI: https://doi.org/10.1016/j.future.2020.04.036
Q. Zheng, S. L. Furth, G. E. Tasian, and Y. Fan, "Computer-Aided Diagnosis of Congenital Abnormalities of the Kidney and Urinary Tract in Children Based on Ultrasound Imaging Data by Integrating Texture Image Features and Deep Transfer Learning Image Features," Journal of Pediatric Urology, vol. 15, no. 1, Feb. 2019, Art. no. 75. DOI: https://doi.org/10.1016/j.jpurol.2018.10.020
K. Venkatrao and S. Kareemulla, "HDLNET: A Hybrid Deep Learning Network Model with Intelligent IOT for Detection and Classification of Chronic Kidney Disease," IEEE Access, vol. 11, pp. 99638–99652, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3312183
Q. Zhang and Q. Wang, "Comparison of Semantic Segmentation Methods on Renal Ultrasound Images," in 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Ottawa, ON, Canada, May 2022, pp. 1–5. DOI: https://doi.org/10.1109/I2MTC48687.2022.9806525
S. H. Song et al., "Deep-Learning Segmentation of Ultrasound Images for Automated Calculation of the Hydronephrosis Area to Renal Parenchyma Ratio," Investigative and Clinical Urology, vol. 63, no. 4, 2022, Art. no. 455. DOI: https://doi.org/10.4111/icu.20220085
S. Valente et al., "A Deep Learning Method for Kidney Segmentation in 2D Ultrasound Images," in 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, United Kingdom, July 2022, pp. 3911–3914. DOI: https://doi.org/10.1109/EMBC48229.2022.9871748
D. Rodríguez-Salas, M. Öttl, M. Seuret, K. Packhäuser, and A. Maier, "Using Forestnets for Partial Fine-Tuning Prior to Breast Cancer Detection in Ultrasounds," in 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia, Apr. 2023, pp. 1–5. DOI: https://doi.org/10.1109/ISBI53787.2023.10230424
S. Sudharson and P. Kokil, "An Ensemble of Deep Neural Networks for Kidney Ultrasound Image Classification," Computer Methods and Programs in Biomedicine, vol. 197, Dec. 2020, Art. no. 105709. DOI: https://doi.org/10.1016/j.cmpb.2020.105709
P. Hao et al., "Texture Branch Network for Chronic Kidney Disease Screening based on Ultrasound Images," Frontiers of Information Technology & Electronic Engineering, vol. 21, no. 8, pp. 1161–1170, Aug. 2020. DOI: https://doi.org/10.1631/FITEE.1900210
Shixuemei and Mideth Abisado, "Diagnostic Segmentation Based on Kidney Medical Image," Journal of Artificial Intelligence and Technology, June 2023. DOI: https://doi.org/10.37965/jait.2023.0214
J. Liu et al., "Asym-UNet: An Asymmetric U-Shape Network for Breast Lesions Ultrasound Images Segmentation," Biomedical Signal Processing and Control, vol. 99, Jan. 2025, Art. no. 106822. DOI: https://doi.org/10.1016/j.bspc.2024.106822
C. Zhang, L. Wang, G. Wei, Z. Kong, and M. Qiu, "A Dual-Branch and Dual Attention Transformer and CNN Hybrid Network for Ultrasound Image Segmentation," Frontiers in Physiology, vol. 15, Sept. 2024, Art. no. 1432987. DOI: https://doi.org/10.3389/fphys.2024.1432987
Y. Xie, B. Yang, Q. Guan, J. Zhang, Q. Wu, and Y. Xia, "Attention Mechanisms in Medical Image Segmentation: A Survey." arXiv, 2023.
C.-P. Fu, M.-J. Yu, Y.-S. Huang, C.-S. Fuh, and R.-F. Chang, "Stratifying High-Risk Thyroid Nodules Using a Novel Deep Learning System," Experimental and Clinical Endocrinology & Diabetes, vol. 131, no. 10, pp. 508–514, Oct. 2023. DOI: https://doi.org/10.1055/a-2122-5585
S. M. Kabir and M. I. H. Bhuiyan, "RiIG-WCP Image-based Breast Tumors Classification Using Swin Transformer," in 2023 26th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, Dec. 2023, pp. 1–5. DOI: https://doi.org/10.1109/ICCIT60459.2023.10440979
Z. Liu et al., "Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows," in 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, Oct. 2021, pp. 9992–10002. DOI: https://doi.org/10.1109/ICCV48922.2021.00986
Y. Fu et al., "Semantic-Attention Enhanced DSC-Transformer for Lymph Node Ultrasound Classification and Remote Diagnostics," Bioengineering, vol. 12, no. 2, Feb. 2025, Art. no. 190. DOI: https://doi.org/10.3390/bioengineering12020190
T. Yu and P. Li, "Degenerate Swin to Win: Plain Window-based Transformer without Sophisticated Operations." arXiv, 2022.
J. Koo, J. Yang, L. An, G. C. Sergio, and S. I. Park, "Swin-Free: Achieving Better Cross-Window Attention and Efficiency with Size-varying Window." arXiv, 2023.
Z. Huang, Y. Ben, G. Luo, P. Cheng, G. Yu, and B. Fu, "Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer." arXiv, 2021.
H. R. Torres, S. Queirós, P. Morais, B. Oliveira, J. C. Fonseca, and J. L. Vilaça, "Kidney Segmentation in Ultrasound, Magnetic Resonance and Computed Tomography Images: A Systematic Review," Computer Methods and Programs in Biomedicine, vol. 157, pp. 49–67, Apr. 2018. DOI: https://doi.org/10.1016/j.cmpb.2018.01.014
X. Liang, M. Du, and Z. Chen, "Artificial Intelligence-Aided Ultrasound in Renal Diseases: A Systematic Review," Quantitative Imaging in Medicine and Surgery, vol. 13, no. 6, pp. 3988–4001, June 2023. DOI: https://doi.org/10.21037/qims-22-1428
S. Valente et al., "A Comparative Study of Deep Learning Methods for Multi-Class Semantic Segmentation of 2D Kidney Ultrasound Images," in 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia, July 2023, pp. 1–4. DOI: https://doi.org/10.1109/EMBC40787.2023.10341170
N. Siddique, S. Paheding, C. P. Elkin, and V. Devabhaktuni, "U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications," IEEE Access, vol. 9, pp. 82031–82057, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3086020
M. A. Isaza-Ruget et al., "Predicting Chronic Kidney Disease Progression with Artificial Intelligence," BMC Nephrology, vol. 25, no. 1, Apr. 2024, Art. no. 148. DOI: https://doi.org/10.1186/s12882-024-03545-7
M. Tounsi, D. Y. Abdulhussain, A. T. Azar, A. Al-Khayyat, and I. K. Ibraheem, "Deep Learning Model-based Decision Support System for Kidney Cancer on Renal Images," Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 17177–17187, Oct. 2024. DOI: https://doi.org/10.48084/etasr.8335
Y. H. Bhosale, K. S. Patnaik, S. R. Zanwar, S. Kr. Singh, V. Singh, and U. B. Shinde, "Thoracic-Net: Explainable Artificial Intelligence (XAI) Based Few Shots Learning Feature Fusion Technique for Multi-Classifying Thoracic Diseases Using Medical Imaging," Multimedia Tools and Applications, vol. 84, no. 9, pp. 5397–5433, Oct. 2024. DOI: https://doi.org/10.1007/s11042-024-20327-3
Y. H. Bhosale, P. Singh, and K. S. Patnaik, "COVID-19 and Associated Lung Disease Classification Using Deep Learning," in International Conference on Innovative Computing and Communications, vol. 492, D. Gupta, A. Khanna, A. E. Hassanien, S. Anand, and A. Jaiswal, Eds. Singapore: Springer Nature Singapore, 2023, pp. 283–295. DOI: https://doi.org/10.1007/978-981-19-3679-1_22
"Internal Ultrasound Dataset for Acute and Chronic Kidney Disease Detection Based on Ultrasound Images." Ministry of National Guard - Health Affairs (MNG-HA) & King Abdullah International Medical Research Center (KAIMRC), King Abdulaziz Hospital, Al Ahsa, Saudi Arabia, https://www.moh.gov.sa/en/Pages/default.aspx, 2021.
A. Buslaev, V. I. Iglovikov, E. Khvedchenya, A. Parinov, M. Druzhinin, and A. A. Kalinin, "Albumentations: Fast and Flexible Image Augmentations," Information, vol. 11, no. 2, Feb. 2020, Art. no. 125. DOI: https://doi.org/10.3390/info11020125
M. Yeung, E. Sala, C.-B. Schönlieb, and L. Rundo, "Unified Focal Loss: Generalising Dice and Cross Entropy-Based Losses to Handle Class Imbalanced Medical Image Segmentation," Computerized Medical Imaging and Graphics, vol. 95, Jan. 2022, Art. no. 102026. DOI: https://doi.org/10.1016/j.compmedimag.2021.102026
M. Tan and Q. V. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks," 2019.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely Connected Convolutional Networks," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, July 2017, pp. 2261–2269. DOI: https://doi.org/10.1109/CVPR.2017.243
G. Kaur and S. Singh, "Kidney Ultrasound Images 'Stone' and 'No Stone.'" Mendeley Data, Aug. 27, 2024.
X. Luo, M. Hu, T. Song, G. Wang, and S. Zhang, "Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer." arXiv, 2021.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, "Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization," International Journal of Computer Vision, vol. 128, no. 2, pp. 336–359, Feb. 2020. DOI: https://doi.org/10.1007/s11263-019-01228-7
M. Mitchell et al., "Model Cards for Model Reporting," in Proceedings of the Conference on Fairness, Accountability, and Transparency, Atlanta, GA, USA, Jan. 2019, pp. 220–229. DOI: https://doi.org/10.1145/3287560.3287596
A. S. Tejani et al., "Updating the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) for Reporting AI Research," Nature Machine Intelligence, vol. 5, no. 9, pp. 950–951, Sept. 2023. DOI: https://doi.org/10.1038/s42256-023-00717-2
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