Deep learning-based framework for the distinction of membranous nephropathy: A new approach through hyperspectral imagery
Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, in some cases, the morphologic differences are not visible under the light microscope in the renal biopsy tissue. Methods We proposed a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% for multiclass classification, which also proven to obtain a better performance compared to the support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN could be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid the diagnosis process of MN.