Research on an Optimized Deep Learning-Based Classification Model for Ovarian Cyst Ultrasound Images

Background Ovarian cysts are a common pelvic disorder in women, and accurate differentiation between benign and malignant types is essential for guiding treatment decisions and prognostic evaluations. However, traditional ultrasound examinations heavily depend on the operator's experience, introducing subjectivity and diagnostic inconsistencies. In recent years, deep learning technologies have demonstrated strong potential in intelligent medical imaging diagnostics, offering innovative solutions for automated and precise classification of ovarian cysts. Results Compared to subjective evaluations by senior ultrasound physicians (accuracy: 76.5%) and the O-RADS classification system (accuracy: 87.8%), the DenseNet121 model exhibited significant advantages in overall diagnostic efficacy (P < 0.05). Conclusions Deep learning models based on ultrasound images can effectively address noise and feature complexity in such imaging, enabling high-precision classification of benign and malignant ovarian cysts. These models hold strong potential for clinical adoption, providing physicians with objective and reliable decision-making support.