Deep learning-based framework for the distinction of membranous nephropathy: A new approach through hyperspectral imagery
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, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues.
Methods: We propose here 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% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms.
Conclusion: IMN and HBV-MN can 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 in the diagnosis of MN.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Posted 19 Jan, 2021
Received 06 Feb, 2021
Received 29 Jan, 2021
On 13 Jan, 2021
On 11 Jan, 2021
On 10 Jan, 2021
Invitations sent on 10 Jan, 2021
On 10 Jan, 2021
On 10 Jan, 2021
Received 25 Nov, 2020
On 25 Nov, 2020
On 07 Nov, 2020
Received 05 Oct, 2020
On 09 Sep, 2020
Received 06 Sep, 2020
On 09 Jul, 2020
Invitations sent on 06 Jul, 2020
On 06 Jul, 2020
On 05 Jul, 2020
On 05 Jul, 2020
On 07 Jun, 2020
On 07 Jun, 2020
On 06 Jun, 2020
On 06 Jun, 2020
Deep learning-based framework for the distinction of membranous nephropathy: A new approach through hyperspectral imagery
Posted 19 Jan, 2021
Received 06 Feb, 2021
Received 29 Jan, 2021
On 13 Jan, 2021
On 11 Jan, 2021
On 10 Jan, 2021
Invitations sent on 10 Jan, 2021
On 10 Jan, 2021
On 10 Jan, 2021
Received 25 Nov, 2020
On 25 Nov, 2020
On 07 Nov, 2020
Received 05 Oct, 2020
On 09 Sep, 2020
Received 06 Sep, 2020
On 09 Jul, 2020
Invitations sent on 06 Jul, 2020
On 06 Jul, 2020
On 05 Jul, 2020
On 05 Jul, 2020
On 07 Jun, 2020
On 07 Jun, 2020
On 06 Jun, 2020
On 06 Jun, 2020
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, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues.
Methods: We propose here 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% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms.
Conclusion: IMN and HBV-MN can 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 in the diagnosis of MN.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8