Combining convolutional operators in unsupervised networks for kidney abnormalities

Abstract

Deep learning plays a pivotal role in advancing the diagnosis of renal dysfunction, achieving performance levels comparable to those of medical experts. However, disease domain variations and model differences can impact learning quality. To address renal dysfunction, we propose dual stream convolutional (DSC) and dual-input convolutional (DIC) for unsupervised learning. The proposed network is designed to process multi scale data and employs parallel data aggregation to enhance learning capabilities, improving the reliability of the experimental results. DSC achieved training losses of 0.0069, 0.0056, 0.0042, and 0.0048 for normal, cyst, stone, and tumor datasets, respectively, while DIC achieved losses of 0.0066, 0.0063, 0.0044, and 0.0058 for the same categories. The experimental results demonstrate that our proposed models outperform state of-the-art approaches, making them well-suited for broad application in clinical research studies.