Monitoring kidney microanatomy during ischemia-reperfusion using ANFIS optimized CNN

Kidney disease is a dangerous disease that affects human health and causes various defects. Renal microbiological changes can be monitored using optical coherence tomography (OCT) images to identify the nature of the disease based on behavior during ischemia-reperfusion. Image analysis becomes the more sophisticated part of extracting information from feature dependencies from objects to identify the disease. Most methodologies use feature correlation dependencies in non-relation feature analysis-based disease identification with low precision and recall level. So, classification accuracy needs to be higher performance. To resolve this problem, we proposed the adaptive neuro-fuzzy inference system-based Resnet50 optimal convolutional neural network (ANFIS-CNN) method implemented using deep learning (DL) to monitor kidney disease. Initially, we analyze using OCT images collected from a standard repository. Furthermore, bidirectional filters can be used for preprocessing to reduce image noise. Gaussian filtering can be applied to identify the dependence of kidney structure. Afterward, the color density saturation can be analyzed through edge-based segmentation using the histogram equalization method, and the optimally extracted objects can be identified through edge-based segmentation. These spectral value-based relative feature detection thresholds are combined with texture point-based recursive spectral multiscale feature selection (RSMFS) to produce different entity contrasts. Then, spectral values are optimized with ANFIS-Resnet50 optimal CNN to classify the accuracy by selecting images. Moreover, the proposed method results in high classification accuracy up to 96.1 %, recall rate 95.18 % and precision up to 96.09 % well attained, enhancing their overall performance. The system develops high-performance image recognition for kidney disease monitoring.