Automating Kidney Disease Diagnosis: A Segmentation-Based, Dual-Input Approach for Classifying Acute and Chronic Kidney Disease from Ultrasound Images

Abstract

Chronic Kidney Disease (CKD) and Acute Kidney Injury (AKI) are serious health conditions, where ultrasound imaging serves as a non-invasive diagnostic tool. Diagnosis relies on identifying significant morphological features, such as a shrunken appearance in CKD or an enlarged, echogenic kidney in AKI. This study proposes and evaluates a two-stage deep learning framework developed using a clinical dataset collected from the Saudi Ministry of National Guard Health Affairs (NGHA). The process utilizes a modified U-Net model for precise kidney segmentation, followed by a novel dual-input classification stage that integrates both the original and segmented images to enhance feature extraction. A comprehensive analysis was conducted on four advanced architectures, including a Swin Transformer, DenseNet121, EfficientNetB0, and ResNet50. The results demonstrated that the ResNet50 model with dual-input configuration, preceded by SS-MUNet segmentation, was highly effective, achieving a test accuracy of 82.88% and an F1-score of 78.36%. Dual-input setups consistently outperformed single-input variants by 10-15% in F1-score across architectures, with ResNet50 and DenseNet121 showing the strongest generalization. This approach enhances diagnostic performance by using both segmented and non-segmented images to distinguish pathological kidney features with high accuracy. The findings establish a robust framework that holds considerable potential as a speedy, reliable, and accessible decision-support tool for clinical practice in nephrology. The proposed dual-input framework based on both original and segmented kidney images improves macro F1-score by an average of 12.5% in the range of 10.2–15.3% and accuracy by 9–13% compared to single-input baselines across four modern architectures on the independent test set. This clinically significant gain enables more reliable automated differentiation of AKI, CKD, and normal kidneys using only standard ultrasound.