A DWT-Utilized Classifier for UPJO Diagnosis Using Ultrasound Images.

Ureteropelvic Junction Obstruction (UPJO) is a common hydronephrosis disease in children that can result in even progressive loss of renal function. Ultrasonography as a preliminary diagnostic step for UPJO has the nature of economical, radiationless, noninvasive, and high-noise. Artificial intelligence has been widely applied to medical fields and can greatly assistant for doctors' diagnostic ability. We build and test a DWT-utilized classifier for UPJO diagnosis using ultrasound images. Our diagnosis model is a combination of an attention-based pyramid semantic segmentation network and a discrete wavelet transformation processed residual classification network. We also compare the performance between benchmark models and our models. Our diagnosis model outperformed benchmarks on classification task with accuracy=91.77%. This model can automatically grade the severity of UPJO by ultrasound images, assistant for doctors' diagnostic ability, and relieve patients' burden.