Accurate Ovarian Cyst Classification With a Lightweight Deep Learning Model for Ultrasound Images

The ovarian cyst is a prevalent disease among women of childbearing age. Early detection of ovaries can effectively prevent the risk of large cysts leading to torsion, infertility, and even progression to ovarian cancer. Ultrasonography is a common method for screening ovarian cysts. However, as the demand for ultrasound has exploded in recent years, doctors' workloads have undoubtedly increased. The ultrasonic image analysis of ovarian cysts using deep learning is aimed at assisting doctors in rapid diagnosis and providing a good diagnostic decision for patients. We proposed a deep learning network for the classification and diagnosis of ovarian cysts, namely Ocys-Net. This method incorporates a reverse bottleneck design strategy and makes full use of global information to improve its feature extraction ability. Meanwhile, the efficient channel attention (ECA) module is used to realize local cross-channel interaction, which pays sufficient attention to pathological information features and effectively makes up for the defects caused by channel dimension reduction. As a lightweight network, the proposed method takes into account the efficient learning performance of the model and is evaluated on our ovarian cyst dataset with high accuracy. The classification accuracy of this network is 95.93%, which has certain practicability in clinical application.