Kidney diseases such as chronic kidney disease endanger human health globally and increase the economic burden of society to varying degrees. Therefore, many diagnostic techniques for kidney diseases have emerged, including renal immunofluorescence image diagnosis. Currently, deep learning is widely applied to various medical image processing tasks, especially in renal immunofluorescence image processing, which has achieved favorable application effects. In this paper, the application of deep learning to renal immunofluorescence image is reviewed, and the application progress on deep learning in glomerular target detection, target segmentation, and morphological classification is explored, aiming at exploring application potentiality of deep learning in kidney disease research, and providing new ideas for the development of diagnostic techniques of kidney diseases. 

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