OBJECTIVE: To develop deep learning (DL) prediction models using transvaginal ultrasound (TVS), transabdominal ultrasound (TAS), and color Doppler flow imaging (CDFI) of TVS (CDFI_TVS) to automatically predict benign or malignant ovarian tumors. METHODS: This retrospective study included women with ovarian tumors who underwent ultrasound between August 2018 and October 2022. Histopathological analysis was used as a reference standard. The dataset was preprocessed by clipping, flipping, and rotating images to generate a larger, more complicated, and diverse dataset to improve accuracy and generalizability. The dataset was then divided into training (80%) and test (20%) sets. The weights of the models, modified from the residual network (ResNet) with the TVS, TAS, and CDFI_TVS images (hereafter, referred to as DL , DL , and DL , respectively) were developed. The area under the receiver operating characteristic curve (AUC) analysis in the test set was used to compare the predictive value of DL for malignancy. RESULTS: A total of 2340 images from 1350 women with adnexal masses were included. DL had an AUC of 0.95 (95% CI: 0.93-0.97) for classifying malignant and benign ovarian tumors, comparable with that of DL (AUC, 0.95; 95% CI: 0.91-0.98; p = 0.96) and DL (AUC, 0.88; 95% CI: 0.84-0.93; p = 0.02). Decision curve analysis indicated that DL performed better than DL and DL . CONCLUSION: We developed DL models based on TVS, TAS, and CDFI_TVS on ultrasound images to predict benign and malignant ovarian tumors with high diagnostic performance. The DL model had the best prediction compared with the DL and DL models.