Extreme Learning Machine Denoising Algorithm Based Analysis of Transvaginal 3-Dimensional Ultrasonic Image for the Diagnostic Effect of Intrauterine Adhesion

Abstract

The aim was to analyze the application values and diagnostic effects of transvaginal 3-dimensional (3D) ultrasonic image based on extreme learning machine denoising algorithm (ELMDA) in the diagnosis of intrauterine adhesions (IUA). The speckle noise in the 3D ultrasound image was removed with the ELMDA. Its peak signal-to-noise ratio (PSNR) and the mean square error (MSE) were compared with those of the median filter algorithm (MFA) with the anisotropic diffusion algorithm (ADA) and wavelet threshold. The ELMDA was used in the diagnosis of 3D ultrasound images to compare the accuracy of hysteroscopy with transvaginal 3D ultrasound and two-dimensional (2D) ultrasound in the diagnosis of IUA. The results showed that the MSE of ELMDA was dramatically smaller than those of ADA and WT-MFA and its PSNR was higher than those of the other two algorithms ( $P$  < 0.05) when the noise variance was constant. The diagnostic accuracy of mild and moderate adhesions by 2D ultrasound was statistically different ( $P$  < 0.05) compared with hysteroscopy. But the diagnosis results of severe adhesions were consistent, and the diagnosed cases were both 6 (11.11%) with no statistical difference ( $P$  > 0.05). In addition, there was no statistically great difference in the diagnostic accuracy of IUA by transvaginal 3D ultrasound and hysteroscopy ( $P$  > 0.05), and the diagnosis results of moderate and severe adhesions were consistent (both 20 cases (37.04%) and 6 cases (11.11%), respectively) with no statistical difference ( $P$  > 0.05). The diagnostic accuracy of 3D ultrasound was 96.30%, while that of 2D ultrasound was 90.74%, showing a statistical difference ( $P$  < 0.05). In conclusion, ELMDA had a good effect of denoising, and there was a high accuracy of the application of 3D transvaginal ultrasound to diagnose IUA, which had reliable clinical application value.