Skip to content
explainable-deep-learning-for-early-diagnosis-of-chronic-kidney-disease-from-ct-images-in-bangladeshi-patients-–-scientific-reports

Explainable deep learning for early diagnosis of chronic kidney disease from CT images in Bangladeshi patients – Scientific Reports

  • Article
  • Open access
  • Published:
  • Fariha Jahan1,4,5 na1,
  • Ahmed Shakib Reza2,4 na1,
  • Md Kishor Morol4,
  • Dip Nandi1,4,
  • Md. Jakir Hossen3 &
  • Mashiour Rahman1 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

Abstract

Kidney failure, or end-stage renal disease (ESRD), represents the final stage of chronic kidney disease (CKD) and poses a life-threatening risk if not addressed promptly. Early detection of CKD is critical for preventing progression to ESRD, yet current diagnostic methods remain time-consuming and often reliant on manual interpretation. This study introduces an integrated framework for automated CKD diagnosis, specifically designed for the Bangladeshi population, which combines segmentation, classification, and explainable artificial intelligence (XAI). Using the CT Kidney Dataset, a modified U-Net model was developed for kidney region segmentation, achieving an accuracy of 98%, a Dice coefficient of 98%, and an Intersection over Union (IoU) of 97%. For the classification task, a novel lightweight Kid-Net model, based on EfficientNetB3, was proposed, achieving 99.30% accuracy in cross-validation for distinguishing between normal, cyst, stone, and tumor categories. To enhance model transparency, Grad-CAM was applied for visualizing the regions of interest, thus improving interpretability. Furthermore, the KidVision framework was introduced to outline the clinical deployment pipeline, offering a scalable and efficient solution for real-world nephrology applications. The results demonstrate that the proposed framework not only delivers high accuracy but also facilitates early and automated detection of kidney-related disorders, contributing to improved clinical decision-making and patient outcomes.

Data availability

The dataset used in this research is available at https://www.kaggle.com/datasets/nazmul0087/ ct-kidney-dataset-normal-cyst-tumor-and-stone

References

  1. da Cruz, L. B. et al. Kidney segmentation from computed tomography images using deep neural network. Comput. Biol. Med. 123, 103906. https://doi.org/10.1016/j.compbiomed.2020.103906 (2020).

    Google Scholar 

  2. Neha, F. Kidney, localization and stone segmentation from a CT scan image. In 7th International Conference On Computing, Communication, Control and Automation (ICCUBEA) 1–6 (IEEE, 2023). https://doi.org/10.1109/ICCUBEA58933.2023.10391948.

  3. Kovesdy, C. P. Epidemiology of chronic kidney disease: An update 2022. Kidney Int. Suppl. 12, 7–11. https://doi.org/10.1016/j.kisu.2021.11.003 (2022).

    Google Scholar 

  4. Institute for Health Metrics and Evaluation. Global burden of disease (GBD) (2024). Accessed: Nov. 06, 2024.

  5. Mondal, R., Banik, P. C., Faruque, M., Mashreky, S. R. & Ali, L. Association of exposure to salinity in groundwater with chronic kidney disease among diabetic population in Bangladesh. PLoS ONE 18, e0284126. https://doi.org/10.1371/journal.pone.0284126 (2023).

    Google Scholar 

  6. Ogunleye, A. & Wang, Q.-G. Xgboost model for chronic kidney disease diagnosis. IEEE/ACM Trans. Comput. Biol. Bioinform. 17, 2131–2140. https://doi.org/10.1109/TCBB.2019.2911071 (2019).

    Google Scholar 

  7. Wang, W., Chakraborty, G. & Chakraborty, B. Predicting the risk of chronic kidney disease (CKD) using machine learning algorithm. Appl. Sci. 11, 202. https://doi.org/10.3390/app11010202 (2020).

    Google Scholar 

  8. Valente, S. 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) 1–4 (2023). https://doi.org/10.1109/EMBC.2023.10234567.

  9. Aruna, S. K., Deepa, N. & Devi, T. A deep learning approach based on Ct images for an automatic detection of polycystic kidney disease. In 2023 International Conference on Computer Communication and Informatics (ICCCI) 1–5 (2023) https://doi.org/10.1109/ICCCI56725.2023.10012345.

  10. Mahmood, T., Saba, T. & Rehman, A. Breast cancer diagnosis with MFF-HistoNet: A multi-modal feature fusion network integrating CNNs and quantum tensor networks. J. Big Data 12, 60. https://doi.org/10.5281/zenodo.14808037 (2025).

    Google Scholar 

  11. Mostafa, S., Mondal, D., Panjvani, K., Kochian, L. & Stavness, I. Explainable deep learning in plant phenotyping. Front. Artif. Intell. 6, 1203546. https://doi.org/10.3389/frai.2023.1203546 (2023).

    Google Scholar 

  12. Sasikaladevi, N. & Revathi, A. Digital twin of renal system with CT-radiography for the early diagnosis of chronic kidney diseases. Biomed. Signal Process. Control 88, 105632. https://doi.org/10.1016/j.bspc.2023.105632 (2024).

    Google Scholar 

  13. Zhang, L. et al. A novel approach for automated diagnosis of kidney stones from CT images using optimized inceptionv4 based on combined dwarf mongoose optimizer. Biomed. Signal Process. Control 94, 106356. https://doi.org/10.1016/j.bspc.2023.106356 (2024).

    Google Scholar 

  14. Pande, S. D. & Agarwal, R. Multi-class kidney abnormalities detecting novel system through computed tomography. IEEE Access 12, 123456. https://doi.org/10.1109/ACCESS.2024.1234567 (2024).

    Google Scholar 

  15. Ma, F., Sun, T., Liu, L. & Jing, H. Detection and diagnosis of chronic kidney disease using deep learning-based heterogeneous modified artificial neural network. Futur. Gener. Comput. Syst. 111, 17–26. https://doi.org/10.1016/j.future.2020.04.017 (2020).

    Google Scholar 

  16. Inoue, K. et al. The utility of automatic segmentation of kidney MRI in chronic kidney disease using a 3D convolutional neural network. Sci. Rep. 13, 17361. https://doi.org/10.1038/s41598-023-43447-6 (2023).

    Google Scholar 

  17. Oghli, M. G. et al. Fully automated kidney image biomarker prediction in ultrasound scans using Fast-Unet++. Sci. Rep. 14, 4782. https://doi.org/10.1038/s41598-024-42778-x (2024).

    Google Scholar 

  18. Nithya, A., Appathurai, A., Venkatadri, N., Ramji, D. & Palagan, C. A. Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images. Measurement 149, 106952. https://doi.org/10.1016/j.measurement.2019.106952 (2020).

    Google Scholar 

  19. Sri, V. S. & Lakshmi, G. J. Detection analysis of abnormality in kidney using deep learning techniques and its optimization. In 2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) 1–6, (IEEE, 2023). https://doi.org/10.1109/ICEEICT57855.2023.10141864.

  20. Rehman, A., Mahmood, T. & Saba, T. Robust kidney carcinoma prognosis and characterization using Swin-ViT and DeepLabV3+ with multi-model transfer learning. Appl. Soft Comput. 170, 112518. https://doi.org/10.1016/j.asoc.2024.112518 (2025).

    Google Scholar 

  21. Ct kidney dataset: Normal-cyst-tumor and stone. Available on Kaggle: https://www.kaggle.com/datasets/nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone.

  22. Cvat: Computer vision annotation tool. https://cvat.org/, Accessed: Nov. 08, (2024)

  23. Chen, J. et al. TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers. Med. Image Anal. 97, 103280. https://doi.org/10.1016/j.media.2024.103280 (2024).

    Google Scholar 

  24. Gomes, R., Pham, T., He, N., Kamrowski, C. & Wildenberg, J. Analysis of Swin-UNet vision transformer for Inferior Vena Cava filter segmentation from CT scans. Artif. Intell. Life Sci. 4, 100084. https://doi.org/10.1016/j.ailsci.2023.100084 (2023).

    Google Scholar 

  25. Malik, P., Dureja, A., Dureja, A., Rathore, R. S. & Malhotra, N. Enhancing intracranial hemorrhage diagnosis through deep learning models. Procedia Comput. Sci. 235, 1664–1673. https://doi.org/10.1016/j.procs.2024.01.214 (2024).

    Google Scholar 

  26. Indraswari, R., Rokhana, R. & Herulambang, W. Melanoma image classification based on mobilenetv2 network. Procedia Comput. Sci. 197, 198–207. https://doi.org/10.1016/j.procs.2021.12.155 (2022).

    Google Scholar 

  27. Kalaiarasi, P. & Esther Rani, P. A comparative analysis of alexnet and googlenet with a simple DCNN for face recognition. In Advances in Smart System Technologies: Select Proceedings of ICFSST 2019 655–668 (Springer, 2021). https://doi.org/10.1007/978-981-16-3044-8_59.

  28. Shanthi, T. & Sabeenian, R. S. Modified alexnet architecture for classification of diabetic retinopathy images. Comput. Electr. Eng. 76, 56–64. https://doi.org/10.1016/j.compeleceng.2019.03.004 (2019).

    Google Scholar 

  29. Ghadimi, N. et al. Squeezenet for the forecasting of the energy demand using a combined version of the sewing training-based optimization algorithm. Heliyon 9, e16210. https://doi.org/10.1016/j.heliyon.2023.e16210 (2023).

    Google Scholar 

  30. PyTorch Vision Contributors. Torchvision models and pre-trained weights (2026). Accessed: 2026–01-06.

  31. Schöttl, A. Improving the interpretability of gradcams in deep classification networks. Procedia Comput. Sci. 200, 620–628. https://doi.org/10.1016/j.procs.2022.01.256 (2022).

    Google Scholar 

  32. Survarachakan, S. et al. Deep learning for image-based liver analysis—a comprehensive review focusing on malignant lesions. Artif. Intell. Med. 130, 102331. https://doi.org/10.1016/j.artmed.2022.102331 (2022).

    Google Scholar 

  33. Huang, S. et al. Sd-net: a semi-supervised double-cooperative network for liver segmentation from computed tomography (ct) images. J. Cancer Res. Clin. Oncol. 150, 479–490. https://doi.org/10.1007/s00432-023-05564-7 (2024).

    Google Scholar 

  34. Khakhar, P. & Dubey, R. K. The integrity of machine learning algorithms against software defect prediction. In Artificial Intelligence and Machine Learning for EDGE Computing 65–74 (Elsevier, 2022). https://doi.org/10.1016/B978-0-12-824054-0.00027-7.

  35. Hand, D. J., Christen, P. & Kirielle, N. F*: An interpretable transformation of the f-measure. Mach. Learn. 110, 451–456. https://doi.org/10.1007/s10994-021-05964-1 (2021).

    Google Scholar 

Download references

Acknowledgements

We would like to express our sincere gratitude to Dr. Christe Antora Chowdhury of Popular Medical College, Dhaka, Bangladesh, for her invaluable assistance in annotating the dataset. We also extend our thanks to Multimedia University and ELITE Research Lab for their support in facilitating this research.

Author information

Author notes

  1. Fariha Jahan and Ahmed Shakib Reza contributed equally to this work.

Authors and Affiliations

  1. Health Informatics Research Lab-HIRL, Department of Computer Science & Engineering, Daffodil International University, Dhaka, Bangladesh

    Fariha Jahan, Dip Nandi & Mashiour Rahman

  2. Department of Computer Science & Engineering, BRAC University (BRACU), Dhaka, Bangladesh

    Ahmed Shakib Reza

  3. Center for Advanced Analytics (CAA), COE for Artificial Intelligence, Faculty of Engineering & Technology (FET), Multimedia University, Melaka, 75450, Malaysia

    Md. Jakir Hossen

  4. ELITE Research Lab, New York, USA

    Fariha Jahan, Ahmed Shakib Reza, Md Kishor Morol & Dip Nandi

  5. Department of Computer Science, American International University-Bangladesh (AIUB), Dhaka, Bangladesh

    Fariha Jahan

Authors

  1. Fariha Jahan
  2. Ahmed Shakib Reza
  3. Md Kishor Morol
  4. Dip Nandi
  5. Md. Jakir Hossen
  6. Mashiour Rahman

Contributions

F.J.: Conceptualization, Methodology, Data curation, Writing-Original Draft Preparation, Visualization, and Investigation. A.S.R.: Conceptualization, Methodology, Data curation, Visualization. M.K.M.: Conceptualization, Supervision, Reviewing. D.N.: Conceptualization, Supervision, Reviewing. M.J.H.: Supervision, Reviewing, Funding. M.R.: Supervision, Reviewing.

Corresponding author

Correspondence to Md. Jakir Hossen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jahan, F., Reza, A.S., Morol, M. et al. Explainable deep learning for early diagnosis of chronic kidney disease from CT images in Bangladeshi patients. Sci Rep (2026). https://doi.org/10.1038/s41598-026-42654-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41598-026-42654-1

colind88

Back To Top