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.

免疫球蛋白A（IgA）-肾病（IgAN）是肾衰竭的主要原因之一。它为估算终末期肾脏疾病的分期和增殖率提供了重要的线索。IgA分期可以借助MEST-C评分进行估算。从整个滑动肾脏图像中手动估算MEST-C评分是一项非常繁琐且困难的任务。这项研究使用一些卷积神经网络（CNN）相关模型来检测MEST-C中的肾小球系膜细胞过多性（M评分）。CNN无需分析数据即可直接从图像数据中学习特征。当图像数据大小对于特定类别足够大时，可以有效地训练CNN。在较小的数据大小的情况下，可以有效地使用传输学习，其中在某些普通图像上然后在主题图像上对CNN进行预训练。由于数据集大小很小，节省了收集大型数据集所花费的时间。由于模型已经过预训练，因此转移学习的训练时间也减少了。这项研究工作旨在通过利用转移学习从具有小数据量的活检图像中检测肾小球系膜细胞过多。这项研究工作中使用的数据集包括从德里的全印度医学科学研究所收到的138例IgA患者的单个肾小球（×20放大倍数活检）图像。在这里，将机器学习（k近邻（KNN）和支持向量机（SVM））分类器与转移学习CNN方法进行了比较。深度学习的图像特征由机器学习分类器使用。不同的评估参数已用于将基本分类器的预测与深度学习模型进行比较。研究工作得出的结论是，与使用小数据集的KNN，SVM方法相比，迁移学习深度CNN方法可以改善肾小球系膜细胞过多的检测。该模型可以帮助病理学家了解肾衰竭的阶段。