Vol 7 No 2 (2025)

• Analysis of Prostate Contrast-Enhanced Ultrasonography Images Based on Deep Learning

  De-Jun Hu; Kang He, Jin Li

  Prostate diseases seriously affect men's health, and contrast-enhanced ultrasonography imaging technology plays an important role in the diagnosis of prostate diseases. However, traditional image analysis methods have certain limitations when dealing with prostate contrast-enhanced ultrasonography images. This study aims to utilize the powerful feature learning and pattern recognition capabilities of deep learning to conduct precise analysis on prostate contrast-enhanced ultrasonography images. By constructing an appropriate deep learning model architecture, collecting and sorting out a large number of prostate contrast-enhanced ultrasonography image datasets, and carrying out model training, verification and testing. The experimental results show that, compared with traditional methods, the proposed deep learning method demonstrates higher accuracy and efficiency in aspects such as the identification of prostate lesions, the delineation of boundaries and the analysis of contrast agent perfusion characteristics, and is expected to provide a powerful technical support for the early diagnosis and condition assessment of prostate diseases.

  PDF