Diagnostic efficacy of ultrasound combined with magnetic resonance imaging in diagnosis of deep pelvic endometriosis under deep learning

Abstract

This article was to explore the application of deep learning algorithms in the classification and diagnosis of deep pelvic endometriosis (DPE) by ultrasound and magnetic resonance imaging (MRI). Vaginal ultrasound (VU) and MRI images of 118 patients with DPE and 206 patients with other gynaecological diseases who were treated in our hospital from January 2015 to January 2018 were analysed. Firstly, the global average pooling (GAP) was introduced based on the visual geometry group (VGG) network to design the VGG-GAP model for VU image recognition. Next, the C3D model was improved as the IC3D model for MRI image recognition. The diagnostic values of patients with DPE based on classified VU and MRI images were compared and analysed. The results revealed that the classification accuracy of the VGG-GAP model in the VU image was 96.5%, and the classification accuracy of the IC3D model in the MRI image was 99.2%. The diagnosis accuracy using VU images and MRI images was 90.68% and 92.37%, respectively. Based on this, the classification of VU or MRI images based on deep learning algorithms could provide a basis for improving the diagnosis efficiency of DPE. The value of MRI in the diagnosis of DPE was significantly higher than that of VU.