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Detection of Mesangial hypercellularity of MEST-C score in immunoglobulin A-nephropathy using deep convolutional neural network

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Abstract

Immunoglobulin A (IgA)-nephropathy (IgAN) is one of the major reasons for renal failure. It provides vital clues to estimate the stage and the proliferation rate of end-stage kidney disease. IgA stage can be estimated with the help of MEST-C score. The manual estimation of MEST-C score from whole slide kidney images is a very tedious and difficult task. This study uses some Convolutional neural networks (CNNs) related models to detect mesangial hypercellularity (M score) in MEST-C. CNN learns the features directly from image data without the requirement of analytical data. CNN is trained efficiently when image data size is large enough for a particular class. In the case of smaller data size, transfer learning can be used efficiently in which CNN is pre-trained on some general images and then on subject images. Since the data set size is small, time spent in collecting large data set is saved. The training time of transfer learning is also reduced because the model is already pre-trained. This research work aims at the detection of mesangial hypercellularity from biopsy images with small data size by utilizing the transfer learning. The dataset used in this research work consists of 138 individual glomerulus (× 20 magnification digital biopsy) images of IgA patients received from All India Institute of Medical Science, Delhi. Here, machine learning (k-nearest neighbour (KNN) and support vector machine (SVM)) classifiers are compared to transfer learning CNN methods. The deep extracted image features are used by machine learning classifiers. The different evaluation parameters have been used for comparing the predictions of basic classifiers to the deep learning model. The research work concludes that the transfer learning deep CNN method can improve the detection of mesangial hypercellularity as compare to KNN, SVM methods when using the small data set. This model could help the pathologists to understand the stages of kidney failure.

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References

  1. Abbas Q, Fondon I, Sarmiento A, Jiménez S, Alemany P (2017) Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features. Med Biol Eng Comput 55(11):1959–1974

    Article  Google Scholar 

  2. Ahn E, Kumar A, Kim J, Li C, Feng D, Fulham M (2016) X-ray image classification using domain transferred convolutional neural networks and local sparse spatial pyramid. In: IEEE 13th ISBI , pp 855–858

  3. Alamartine E, Sabatier JC, Guerin C, Berliet JM, Berthoux F (1991) Prognostic factors in mesangial IgA glomerulonephritis: an extensive study with univariate and multivariate analyses. Am J Kidney Dis 18(1):12–19

    Article  Google Scholar 

  4. Alghamdi A, Hammad M, Ugail H, Abdel-Raheem A, Muhammad K, Khalifa HS, Abd El-Latif AA (2019) Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities. Multimed Tools Appl 1–22

  5. Alghamdi AS, Polat K, Alghoson A, Alshdadi AA, Abd El-Latif A (2020) A novel blood pressure estimation method based on the classification of oscillometric waveforms using machine-learning methods. Appl Acoust 164

  6. Alghamdi AS, Polat K, Alghoson A, Alshdadi AA, Abd El-Latif A (2020) Gaussian process regression (GPR) based non-invasive continuous blood pressure prediction method from cuff oscillometric signals. Appl Acoust 164

  7. Avci E (2009) A new intelligent diagnosis system for the heart valve diseases by using genetic-SVM classifier. Expert Syst Appl 36(7):10618–10626

    Article  Google Scholar 

  8. Barbour SJ, Espino-Hernandez G, Reich HN, Coppo R, Roberts IS, Feehally J, Herzenberg AM, Cattran DC, Bavbek N, Cook T, Troyanov S (2016) The MEST score provides earlier risk prediction in lgA nephropathy. Kidney Int 89(1):167–175

    Article  Google Scholar 

  9. Bartosik LP, Lajoie G, Sugar L, Cattran DC (2001) Predicting progression in IgA nephropathy. Am J Kidney Dis 38(4):728–735

    Article  Google Scholar 

  10. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

    Article  Google Scholar 

  11. Berthoux F, Mohey H, Laurent B, Mariat C, Afiani A, Thibaudin L (2011) Predicting the risk for dialysis or death in IgA Nephropathy. J Am Soc Nephrol 22(4):752–761

    Article  Google Scholar 

  12. Cai W, Wei Z (2020) PiiGAN: generative adversarial networks for pluralistic image inpainting. IEEE Access 8:48451–48463

    Article  Google Scholar 

  13. Cannone R, Castiello C, Fanelli AM, Mencar C (2011) Assessment of semantic cointension of fuzzy rule-based classifiers in a medical context. In: 11th international conference on intelligent systems design and applications, Cordoba, pp 1353–1358

  14. Cattran DC, Coppo R, Cook HT, Feehally J, Roberts IS, Troyanov S, Alpers CE, Amore A, Barratt J (2009) The Oxford classification of IgA nephropathy: rationale, clinicopathological correlations, and classification. Kidney Int 76(5):534–545

    Article  Google Scholar 

  15. Cristianini N, Shave-Taylor J (2000) An introduction to support vector machine and other kernel-based learning methods. Cambridge University Press, New York

    Book  Google Scholar 

  16. Cruz-Ramírez M, Hervás-Martínez C, Fernández JC, Briceno J, delaMata M (2013) Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks. Artif Intell Med 58 (1):37–49

    Article  Google Scholar 

  17. El-Rahiem BA, Ahmed MAO, Reyad O, El-Rahaman HA, Amin M, El-Samie FA (2019) An efficient deep convolutional neural network for visual image classification. In: International conference on advanced machine learning technologies and applications, pp 23–31

  18. Escudero J, Ifeachor E, Zajicek JP, Green C, Shearer J, Pearson S (2013) Machine Learning-Based Method for Personalized and Cost-Effective Detection of Alzheimer’s Disease. IEEE Trans Biomed Eng 60(1):164–168

    Article  Google Scholar 

  19. Esposito M, DeFalco I, DePietro G (2011) An evolutionary, fuzzy DSS for assessing health status in multiple sclerosis disease. Int J Med Inform 80 (12):245–254

    Article  Google Scholar 

  20. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM (2017) Sebastian Thrun Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118

    Article  Google Scholar 

  21. Fan DP, Liu J, Gao S, Hou Q, Borji A, Cheng MM (2018) Salient objects in clutter: bringing salient object detection to the foreground. CoRR

  22. Fu K, Zhao Q, Gu Irene Yu-Hua, Yang J (2019) Deepside: A general deep framework for salient object detection. Neurocomputing 356:69–82

    Article  Google Scholar 

  23. Gao Z, Zhang J, Zhou L, Wang L (2014) HEp-2 cell image classification with CNN. In: Pattern recognition techniques for indirect immunofluorescence images (I3A). 1st IEEE Workshop on 24–28

  24. Geddes C, Fox J, Allison M, Boulton-Jones J, Simpson K (1998) An artificial neural network can select patients at high risk of developing progressive IgA nephropathy more accurately than experienced nephrologists. Nephrol Dial Transplant 13(1):67–71

    Article  Google Scholar 

  25. Goto M, Wakai K, Kawamura T, Ando M, Endoh M, Tomino Y (2009) A scoring system to predict renal outcome in IgA nephropathy: a nationwide 10-year prospective cohort study. Nephrol Dial Transplant 24(10):3068–3074

    Article  Google Scholar 

  26. Gray H (1918) Anatomy of the human body, vol 8. Lea & Febiger, Philadelphia

    Google Scholar 

  27. He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. CoRR

  28. Kassahun Y, Perrone R, DeMomi E, Berghöfer E, Tassi L, Canevini MP, Spreafico R, Ferrigno G, Kirchner F (2014) Automatic classification of epilepsy types using ontology-based and genetics-based machine learning. Artif Intell Med 61(2):79–88

    Article  Google Scholar 

  29. Kensert A, Philip JH, Ola S (2019) Transfer learning with deep convolutional neural networks for classifying cellular morphological changes. SLAS 24 (4):466–475

    Google Scholar 

  30. Kirienko Ma, Sollini M, Silvestri G et al (2018) Convolutional neural networks promising in lung cancer t-parameter assessment on baseline FDG-PET/CT. Contrast Media Mol Imaging

  31. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: NIPS’12 proceedings of the 25th international conference on neural information processing systems, pp 1097–1105

  32. Liu M, Cheng D, Yan W (2018) Classification of alzheimer’s disease by combination of convolutional and recurrent neural networks using FDG-PET images. Front Neuroinform 12–35

  33. Lundervold A, Lundervold A (2019) An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik 29 (2):102–127

    Article  Google Scholar 

  34. Ma Y, Chen W, Ma X, Xu J, Huang X, Maciejewski R, Anthony KHT, EasySVM (2017) A visual analysis approach for open-box support vector machines. Comput Vis Med 3(2):161–175

    Article  Google Scholar 

  35. MacKinnon B, Fraser EP, Cattran D, Fox JG, Geddes CC (2008) Validation of the Toronto formula to predict progression in IgA nephropathy. Nephron Clin Pract 109(3):148–153

    Article  Google Scholar 

  36. Maglogiannis E, Anagnostopoulos I (2009) Zafiropoulos An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers. Appl Intell 30(1):24–36

    Article  Google Scholar 

  37. Mandal I, Sairam N (2013) Accurate telemonitoring on Parkinson’s disease diagnosis using robust inference system. Int J Med Inform 82(5):359–377

    Article  Google Scholar 

  38. Nassif AB, Shahin I, Attili I, Azzeh M, Shaalan K (2019) Speech recognition using deep neural networks: a systematic review in IEEE access 7:19143–19165

  39. Noia TD, Ostuni VC, Pesce F, Binetti G, Naso D, Schena FP, Sciascio ED (2013) An end stage kidney disease predictor based on an artificial neural networks ensemble. Expert Syst Appl 40(11):4438–4445

    Article  Google Scholar 

  40. Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35(5):1240–1251

    Article  Google Scholar 

  41. Phan HTH, Kumar A, Kim J, Feng D (2016) Transfer learning of a convolutional neural network for HEp-2 cell image classification. In: IEEE 13th ISBI, pp 1208–1211

  42. Purwar S, Tripathi RK, Ranjan R, Saxena R (2019) Detection of microcytic hypochromia using cbc and blood film features extracted from convolution neural network by different classifiers. Multimed Tools Appl 79:1–23

    Google Scholar 

  43. Radford MG, Donadio JV, Bergstralh EJ, Grande JP (1997) Predicting renal out-come in IgA nephropathy. J Am Soc Nephrol 8(2):199–207

    Google Scholar 

  44. Rauta V, Finne P, Fagerudd J, Rosenlof K, Tornroth T, Gronhagen Riska C (2002) Factors associated with progression of IgA nephropathy are related to renal function-a model for estimating risk of progression in mild disease. Clin Nephrol 58(2):85–94

    Article  Google Scholar 

  45. Roychowdhury S, Koozekanani DD, Parhi KK (2014) DREAM: diabetic retinopathy analysis using machine learning. IEEE J Biomed Health Inform 18(5):1717–1728

    Article  Google Scholar 

  46. Scherer D, Müller A, Behnke S (2010) Evaluation of pooling operations in convolutional architectures for object recognition. In: 20th International Conference on Artificial Neural Networks (ICANN) , pp 92–101

  47. Sheppard D, McPhee D, Darke C, Shrethra B, Moore R, Jurewitz A, Gray A (1999) Predicting cytomegalovirus disease after renal transplantation : an artificial neural network approach. Int J Med Inform 54(1):55–76

    Article  Google Scholar 

  48. Simonyan K (2014) Very deep convolutional networks for large-scale image recognition. Comput Vis Pattern Recognit

  49. Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) Breast cancer histopathological image classification using convolutional neural networks. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp 2560–2567

  50. Szegedy C, et al. (2015) Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, pp 1–9

  51. Szegedy C, Vanhoucke V, Ioffe S, Shlens J (2016) Rethinking the inception architecture for computer vision

  52. Tenório JM, Hummel AD, Cohrs FM, Sdepanian VL, Pisa IT, de Fátima Marin H (2011) Artificial intelligence techniques applied to the development of a decision-support system for diagnosing celiac disease. Int J Med Inform 80(11):793–802

    Article  Google Scholar 

  53. Tortora GJ, Derrickson B (2017) Principles of anatomy and physiology. Wiley, New York

    Google Scholar 

  54. Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E (2018) Deep learning for computer vision: a brief review in computational intelligence and neuroscience

  55. You H, Tian S, Yu L, Lv Y (2020) Pixel-level remote sensing image recognition based on bidirectional word vectors. IEEE Trans Geosci Remote Sens 58(2):1281–1293

    Article  Google Scholar 

  56. Young T, Hazarika D, Poria S, Cambria E (2018) Recent trends in deep learning based natural language processing [review article]. IEEE Comput Intell Mag 13(3):55–75

    Article  Google Scholar 

  57. Zhao JX, Liu J, Fan DP, Cao Y, Yang J (2019) EGNet: edge guidance network for salient object detection. In: IEEE international conference on computer vision

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Acknowledgements

The authors are grateful to department of pathology, All India Institute of Medical Sciences, New Delhi, India, for sharing the dataset. We express gratitude towards Professor of pathology department Dr. A.K. Dinda and his team for the data used in this research and ethical permission number is IEC-48/02.02.2018.

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Authors and Affiliations

  1. Electronics and Communication Department, National Institute of Technology Delhi, Delhi, India

    Shikha Purwar & Rajiv Tripathi

  2. Pathology Department, All India Institute of Medical Sciences, New Delhi, India

    Adarsh Wamanrao Barwad & A. K. Dinda

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  1. Shikha Purwar
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Correspondence to Shikha Purwar.

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Purwar, S., Tripathi, R., Barwad, A.W. et al. Detection of Mesangial hypercellularity of MEST-C score in immunoglobulin A-nephropathy using deep convolutional neural network. Multimed Tools Appl 79, 27683–27703 (2020). https://doi.org/10.1007/s11042-020-09304-8

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