Abstract
Glomerulosclerosis is a common kidney disease characterized by the deposition of scar tissue, which replaces the renal parenchyma, and is quantified by renal pathologists to indicate the presence and extent of renal damage. It is paramount to guide the appropriate treatment and minimize the chances of the disease progressing to chronic stages. Thus, to identify glomerulus with sclerosis, this article proposes a convolutional neural network (CNN) inspired by convolutional blocks of DenseNet-201 but with smaller dense layers. We analyzed five CNNs - VGG-19, Inception-V3, ResNet-50, DenseNet-201, and EfficientNet-B2 - to define the best CNN model and evaluated several configurations for the fully connected layers. In total, 25 different models were analyzed. The experiments were carried out in three datasets, composed of 1,062 images, on which we applied data-augmentation techniques in the training set. These CNNs demonstrated effectiveness in the task and achieved an accuracy of 92.7% and kappa of 85.3%, considered excellent.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
de Araújo, I.C., Schnitman, L., Duarte, A.A., dos Santos, W.: Automated detection of segmental glomerulosclerosis in kidney histopathology. In: XIII Brazilian Congress on Computational Intelligence, p. 12 (2017)
Bueno, G., Fernandez-Carrobles, M.M., Gonzalez-Lopez, L., Deniz, O.: Glomerulosclerosis identification in whole slide images using semantic segmentation. Comput. Methods Programs Biomed. 184, 105273 (2020)
Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20, 37–46 (1960)
Dhaun, N., Bellamy, C., Cattran, D., Kluth, D.: Utility of renal biopsy in the clinical management of renal disease: hematuria should not be missed reply. Kidney Int. 86(6), 1269–1269 (2014)
Ginley, B., et al.: Computational segmentation and classification of diabetic glomerulosclerosis. J. Am. Soc. Nephrol.: JASN 30(10), 1953-1967(2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2015)
Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. CoRR abs/1608.06993 (2016)
Huo, Y., Deng, R., Liu, Q., Fogo, A.B., Yang, H.: AI applications in renal pathology. Kidney Int. 99(6), 1309–1320 (2021)
Kannan, S., et al.: Segmentation of glomeruli within trichrome images using deep learning. Kidney Int. Rep. 4(7), 955–962 (2019)
Kolachalama, V.B.: Association of pathological fibrosis with renal survival using deep neural networks. Kidney Int. Rep. 3(2), 464–475 (2018)
Kornblith, S., Shlens, J., Le, Q.V.: Do better imagenet models transfer better? In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2661–2671 (2019)
van der Laak, J., Litjens, G., Ciompi, F.: Deep learning in histopathology: the path to the clinic. Nat. Med. 27(5), 775–784 (2021)
Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics, 159–174 (1977)
Marsh, J.N., et al.: Deep learning global glomerulosclerosis in transplant kidney frozen sections. IEEE Trans. Med. Imag. 37(12), 2718–2728 (2018)
Pesce, F., et al.: Identification of glomerulosclerosis using IBM Watson and shallow neural networks. J. Nephrol. 35(4), 1235–1242 (2022). https://doi.org/10.1007/s40620-021-01200-0
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115, 211–252 (2015)
Santos, J.D., et al.: A hybrid of deep and textural features to differentiate glomerulosclerosis and minimal change disease from glomerulus biopsy images. Biomed. Signal Process. Control 70, 103020 (2021)
Sheehan, S.M., Korstanje, R.: Automatic glomerular identification and quantification of histological phenotypes using image analysis and machine learning. Am. J. Physiol.-Ren. Physiol. 315(6), F1644–F1651 (2018)
Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016)
Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35, 1299–1312 (2016)
Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning (2019)
Tieleman, T., Hinton, G., et al.: Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Netw. Mach. Learn. 4, 26–31 (2012)
Yi, T.W., et al.: Digital health and artificial intelligence in kidney research: a report from the 2020 Kidney Disease Clinical Trialists (KDCT) meeting. Nephrol. Dial. Transplant. 37(4), 620–627 (2021)
Yu, H., Yang, L.T., Zhang, Q., Armstrong, D., Deen, M.J.: Convolutional neural networks for medical image analysis: State-of-the-art, comparisons, improvement and perspectives. Neurocomputing 444, 92–110 (2021)
Zheng, Y., et al.: Deep-learning-driven quantification of interstitial fibrosis in digitized kidney biopsies. Am. J. Pathol. 191(8), 1442–1453 (2021)
Zheng, Z., et al.: Deep learning-based artificial intelligence system for automatic assessment of glomerular pathological findings in lupus nephritis. Diagnostics 11(11) (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Santos, J. et al. (2022). Glomerulosclerosis Identification Using a Modified Dense Convolutional Network. In: Xavier-Junior, J.C., Rios, R.A. (eds) Intelligent Systems. BRACIS 2022. Lecture Notes in Computer Science(), vol 13653. Springer, Cham. https://doi.org/10.1007/978-3-031-21686-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-21686-2_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21685-5
Online ISBN: 978-3-031-21686-2
eBook Packages: Computer ScienceComputer Science (R0)