Abstract
The prevalence of chronic kidney disease (CKD) is estimated to be 13.4% worldwide and 15% in the United States. CKD has been recognized as a leading public health problem worldwide. Unfortunately, as many as 90% of CKD patients do not know that they already have CKD. Ultrasonography is usually the first and the most commonly used imaging diagnostic tool for patients at risk of CKD. To provide a consistent assessment of the stage classifications of CKD, this study proposes an auxiliary diagnosis system based on deep learning approaches for renal ultrasound images. The system uses the ACWGAN-GP model and MobileNetV2 pre-training model. The images generated by the ACWGAN-GP generation model and the original images are simultaneously input into the pre-training model MobileNetV2 for training. This classification system achieved an accuracy of 81.9% in the four stages of CKD classification. If the prediction results allowed a higher stage tolerance, the accuracy could be improved by up to 90.1%. The proposed deep learning method solves the problem of imbalance and insufficient data samples during training processes for an automatic classification system and also improves the prediction accuracy of CKD stage diagnosis.
Highlights
According to an estimate by the U.S Centers for Disease Control and Prevention (CDC) in 2021, 37 million adults in the United States suffer from chronic kidney disease (CKD) [1]
To verify the transfer learning with the ACWGAN-GP method described in Section 4, we applied several different classification methods to automatically classify the four-stage
We proposed a CKD stage classification system based on integrating the ACWGANGP model and MobileNetV2 pre-training model
Summary
According to an estimate by the U.S Centers for Disease Control and Prevention (CDC) in 2021, 37 million adults in the United States suffer from chronic kidney disease (CKD) [1]. In addition to blood and urine tests, ultrasound is the most commonly used imaging diagnostic tool by nephrologists for the early detection of chronic kidney disease. Ultrasonography is a suitable approach for diagnosing kidney disease because the renal cortex and medulla tissues possess different densities and the difference between the tissues can be observed [2]. Though magnetic resonance angiography (MRA) and computed tomography angiography (CTA) can display clearer vascular images with higher resolution than ultrasonography, and advanced machines can even provide 3D images directly. Compared with MRA and CTA medical imaging, ultrasonography has the advantages of non-ionizing radiation, is non-invasive, safe, portable, inexpensive, and can be operated by medical staff [3,4]. Ultrasonography has become the most commonly used and indispensable imaging tool in nephrology
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