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StoneNet: An Efficient Lightweight Model Based on Depthwise Separable Convolutions for Kidney Stone Detection from CT Images

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Abstract

Kidney stone disease is one of the most common and serious health problems in much of the world, leading to many hospitalizations with severe pain. Detecting small stones is difficult and time-consuming, so an early diagnosis of kidney disease is needed to prevent the loss of kidney failure. Recent advances in artificial intelligence (AI) found to be very successful in the diagnosis of various diseases in the biomedical field. However, existing models using deep networks have several problems, such as high computational cost, long training time, and huge parameters. Providing a low-cost solution for diagnosing kidney stones in a medical decision support system is of paramount importance. Therefore, in this study, we propose “StoneNet”, a lightweight and high-performance model for the detection of kidney stones based on MobileNet using depthwise separable convolution. The proposed model includes a combination of global average pooling (GAP), batch normalization, dropout layer, and dense layers. Our study shows that using GAP instead of flattening layers greatly improves the robustness of the model by significantly reducing the parameters. The developed model is benchmarked against four pre-trained models as well as the state-of-the-art heavy model. The results show that the proposed model can achieve the highest accuracy of 97.98%, and only requires training and testing time of 996.88 s and 14.62 s. Several parameters, such as different batch sizes and optimizers, were considered to validate the proposed model. The proposed model is computationally faster and provides optimal performance than other considered models. Experiments on a large kidney dataset of 1799 CT images show that StoneNet has superior performance in terms of higher accuracy and lower complexity. The proposed model can assist the radiologist in faster diagnosis of kidney stones and has great potential for deployment in real-time applications.

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References

  1. Vupputuri S, Soucie JM, McClellan W, Sandler DP (2004) History of kidney stones as a possible risk factor for chronic kidney disease. Ann Epidemiol 14(3):222–228. https://doi.org/10.1016/S1047-2797(03)00126-1

    Article  PubMed  Google Scholar 

  2. Edvardsson VO, Indridason OS, Haraldsson G, Kjartansson O, Palsson R (2013) Temporal trends in the incidence of kidney stone disease. Kidney Int 83(1):146–152. https://doi.org/10.1038/ki.2012.320

    Article  PubMed  Google Scholar 

  3. Sorokin I, Mamoulakis C, Miyazawa K, Rodgers A, Talati J, Lotan Y (2017) Epidemiology of stone disease across the world. World J Urol 35(9):1301–1320. https://doi.org/10.1007/s00345-017-2008-6

    Article  PubMed  Google Scholar 

  4. Park J, Kang JB, Chang JH, Yoo Y (2014) Speckle reduction techniques in medical ultrasound imaging. Biomed Eng Lett 4(1):32–40. https://doi.org/10.1007/s13534-014-0122-6

    Article  Google Scholar 

  5. Shlipak MG, Fried LF, Cushman M, Manolio TA, Peterson D, Stehman-Breen C et al (2005) Cardiovascular mortality risk in chronic kidney disease: comparison of traditional and novel risk factors. JAMA 293(14):1737–1745. https://doi.org/10.1001/jama.293.14.1737

    Article  CAS  PubMed  Google Scholar 

  6. Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I et al (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298. https://doi.org/10.1109/TMI.2016.2528162

    Article  PubMed  Google Scholar 

  7. Yan K, Wang X, Lu L, Summers RM (2018) DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J Med Imaging 5(3):036501. https://doi.org/10.1117/1.JMI.5.3.036501

    Article  Google Scholar 

  8. Celik Y, Talo M, Yildirim O, Karabatak M, Acharya UR (2020) Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images. Pattern Recogn Lett 133:232–239. https://doi.org/10.1016/j.patrec.2020.03.011

    Article  Google Scholar 

  9. Kott O, Linsley D, Amin A, Karagounis A, Jeffers C, Golijanin D et al (2021) Development of a deep learning algorithm for the histopathologic diagnosis and Gleason grading of prostate cancer biopsies: a pilot study. Eur Urol Focus 7(2):347–351. https://doi.org/10.1016/j.euf.2019.11.003

    Article  PubMed  Google Scholar 

  10. Shkolyar E, Jia X, Chang TC, Trivedi D, Mach KE, Meng MQ-H et al (2019) Augmented bladder tumor detection using deep learning. Eur Urol 76(6):714–718. https://doi.org/10.1016/j.eururo.2019.08.032

    Article  PubMed  PubMed Central  Google Scholar 

  11. Dogan S, Akbal E, Tuncer T, Acharya UR (2021) Application of substitution box of present cipher for automated detection of snoring sounds. Artif Intell Med 117:102085. https://doi.org/10.1016/j.artmed.2021.102085

    Article  PubMed  Google Scholar 

  12. Asif S, Zhao M, Tang F, Zhu Y (2022) A deep learning-based framework for detecting COVID-19 patients using chest X-rays. Multim Syst. https://doi.org/10.1007/s00530-022-00917-7

    Article  Google Scholar 

  13. Cui Y, Sun Z, Ma S, Liu W, Wang X, Zhang X et al (2021) Automatic detection and scoring of kidney stones on noncontrast CT images using STONE nephrolithometry: combined deep learning and thresholding methods. Mol Imag Biol 23(3):436–445. https://doi.org/10.1007/s11307-020-01554-0

    Article  CAS  Google Scholar 

  14. Parakh A, Lee H, Lee JH, Eisner BH, Sahani DV, Do S (2019) Urinary stone detection on CT images using deep convolutional neural networks: evaluation of model performance and generalization. Radiol Artif Intell. https://doi.org/10.1148/ryai.2019180066

    Article  PubMed  PubMed Central  Google Scholar 

  15. Fitri LA, Haryanto F, Arimura H, YunHao C, Ninomiya K, Nakano R et al (2020) Automated classification of urinary stones based on microcomputed tomography images using convolutional neural network. Phys Med 78:201–208. https://doi.org/10.1016/j.ejmp.2020.09.007

    Article  PubMed  Google Scholar 

  16. Jendeberg J, Thunberg P, Lidén M (2021) Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network. Urolithiasis 49(1):41–49. https://doi.org/10.1007/s00240-020-01180-z

    Article  PubMed  Google Scholar 

  17. Längkvist M, Jendeberg J, Thunberg P, Loutfi A, Lidén M (2018) Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks. Comput Biol Med 97:153–160. https://doi.org/10.1016/j.compbiomed.2018.04.021

    Article  PubMed  Google Scholar 

  18. Baygin M, Yaman O, Barua PD, Dogan S, Tuncer T, Acharya UR (2022) Exemplar Darknet19 feature generation technique for automated kidney stone detection with coronal CT images. Artif Intell Med 127:102274. https://doi.org/10.1016/j.artmed.2022.102274

    Article  PubMed  Google Scholar 

  19. Yildirim K, Bozdag PG, Talo M, Yildirim O, Karabatak M, Acharya UR (2021) Deep learning model for automated kidney stone detection using coronal CT images. Comput Biol Med 135:104569. https://doi.org/10.1016/j.compbiomed.2021.104569

    Article  PubMed  Google Scholar 

  20. Blau N, Klang E, Kiryati N, Amitai M, Portnoy O, Mayer A (2018) Fully automatic detection of renal cysts in abdominal CT scans. Int J Comput Assist Radiol Surg 13(7):957–966. https://doi.org/10.1007/s11548-018-1726-6

    Article  PubMed  Google Scholar 

  21. Sudharson S, Kokil P (2020) An ensemble of deep neural networks for kidney ultrasound image classification. Comput Methods Prog Biomed 197:105709. https://doi.org/10.1016/j.cmpb.2020.105709

    Article  CAS  Google Scholar 

  22. Wu Y, Yi Z (2020) Automated detection of kidney abnormalities using multi-feature fusion convolutional neural networks. Knowl Based Syst 200:105873. https://doi.org/10.1016/j.knosys.2020.105873

    Article  Google Scholar 

  23. Haider A, Arsalan M, Lee MB, Owais M, Mahmood T, Sultan H et al (2022) Artificial Intelligence-based computer-aided diagnosis of glaucoma using retinal fundus images. Expert Syst Appl 207:117968. https://doi.org/10.1016/j.eswa.2022.117968

    Article  Google Scholar 

  24. Owais M, Yoon HS, Mahmood T, Haider A, Sultan H, Park KR (2021) Light-weighted ensemble network with multilevel activation visualization for robust diagnosis of COVID19 pneumonia from large-scale chest radiographic database. Appl Soft Comput 108:107490. https://doi.org/10.1016/j.asoc.2021.107490

    Article  PubMed  PubMed Central  Google Scholar 

  25. Arsalan M, Haider A, Cho SW, Kim YH, Park KR (2022) Human blastocyst components detection using multiscale aggregation semantic segmentation network for embryonic analysis. Biomedicines 10(7):1717. https://doi.org/10.3390/biomedicines10071717

    Article  PubMed  PubMed Central  Google Scholar 

  26. Dhillon A, Verma GK (2020) Convolutional neural network: a review of models, methodologies and applications to object detection. Prog Artif Intell 9(2):85–112. https://doi.org/10.1007/s13748-019-00203-0

    Article  Google Scholar 

  27. Suthaharan S (2016) Machine learning models and algorithms for big data classification. Integr Ser Inf Syst 36:1–12. https://doi.org/10.1007/978-1-4899-7641-3

    Article  Google Scholar 

  28. Dunford R, Su Q, Tamang E. The pareto principle. (2014). http://hdl.handle.net/10026.1/14054

  29. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T et al (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv. https://doi.org/10.48550/arXiv.1704.04861

    Article  Google Scholar 

  30. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. Proceed IEEE Int Conf Comput Vis. https://doi.org/10.1109/ICCV.2017.74

    Article  Google Scholar 

  31. Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition. Proceed IEEE Conf Comput Vis Patt Recogn. https://doi.org/10.1109/CVPR.2018.00907

    Article  Google Scholar 

  32. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. Proceed IEEE Conf Comput Vis Patt Recogn. https://doi.org/10.1109/CVPR.2016.308

    Article  Google Scholar 

  33. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv 2: Inverted residuals and linear bottlenecks. Proceed IEEE Conf Computer Vis Patt Recogn. https://doi.org/10.1109/CVPR.2018.00474

    Article  Google Scholar 

  34. Sokolovskaya E, Shinde T, Ruchman RB, Kwak AJ, Lu S, Shariff YK et al (2015) The effect of faster reporting speed for imaging studies on the number of misses and interpretation errors: a pilot study. J Am Coll Radiol 12(7):683–688. https://doi.org/10.1016/j.jacr.2015.03.040

    Article  PubMed  Google Scholar 

  35. Bruno MA, Walker EA, Abujudeh HH (2015) Understanding and confronting our mistakes: the epidemiology of error in radiology and strategies for error reduction. Radiographics 35(6):1668–1676. https://doi.org/10.1148/rg.2015150023

    Article  PubMed  Google Scholar 

  36. Islam MN, Hasan M, Hossain M, Alam M, Rabiul G, Uddin MZ et al (2022) Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography. Sci Rep 12(1):1–14. https://doi.org/10.1038/s41598-022-15634-4

    Article  CAS  Google Scholar 

  37. Rajinikanth V, Vincent PDR, Srinivasan K, Prabhu GA, Chang C-Y (2023) A framework to distinguish healthy/cancer renal CT images using the fused deep features. Front Public Health. https://doi.org/10.3389/fpubh.2023.1109236

    Article  PubMed  PubMed Central  Google Scholar 

  38. Bayram AF, Gurkan C, Budak A, KARATAŞ H. (2022) A Detection and prediction model based on deep learning assisted by explainable artificial intelligence for kidney diseases. Avrupa Bilim ve Teknoloji Dergisi. https://doi.org/10.31590/ejosat.1171777

    Article  Google Scholar 

  39. Alzubi D, Abdullah M, Hmeidi I, AlAzab R, Gharaibeh M, El-Heis M et al (2022) Kidney tumor detection and classification based on deep learning approaches: a new dataset in CT scans. J Healthc Eng. 2022:1–2. https://doi.org/10.1155/2022/3861161

    Article  Google Scholar 

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Funding

This work was supported by the Natural Science Foundation of Hunan Province, China. (Grant No. 2020JJ4757). This work was supported in part by the Intelligent annotation and fine-grained recognition of large-scale multimodal medical behavior belonging to 2030 Innovation Megaprojects (to be fully launched by 2020) through New Generation Artificial. Intelligence Project (Grant 2020AAA0109602), in part by the Key Research and Development Program of Xinjiang Autonomous Region (Grant 2021B01002), in part by the National Key Research and Development Program of China (Grant 2021ZD0140301), and in part by the National Natural Science Foundation of China (Project No. 61902433).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, China

    Sohaib Asif, Ming Zhao & Xuehan Chen

  2. School of Mathematics, Hunan University, Changsha, China

    Yusen Zhu

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  1. Sohaib Asif
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Correspondence to Ming Zhao or Xuehan Chen.

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Asif, S., Zhao, M., Chen, X. et al. StoneNet: An Efficient Lightweight Model Based on Depthwise Separable Convolutions for Kidney Stone Detection from CT Images. Interdiscip Sci Comput Life Sci 15, 633–652 (2023). https://doi.org/10.1007/s12539-023-00578-8

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