Comparison and analysis of deep learning models for discriminating longitudinal and oblique vaginal septa based on ultrasound imaging.

ABSTRACT:

Background

The longitudinal vaginal septum and oblique vaginal septum are female müllerian duct anomalies that are relatively less diagnosed but severely fertility-threatening in clinical practice. Ultrasound imaging is commonly used to examine the two vaginal malformations, but in fact it's difficult to make an accurate differential diagnosis. This study is intended to assess the performance of multiple deep learning models based on ultrasonographic images for distinguishing longitudinal vaginal septum and oblique vaginal septum.

Methods

The cases and ultrasound images of longitudinal vaginal septum and oblique vaginal septum were collected. Two convolutional neural network (CNN)-based models (ResNet50 and ConvNeXt-B) and one base resolution variant of vision transformer (V

SUBMITTER: Wang X 

PROVIDER: S-EPMC11665035 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature