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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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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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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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