Objective This study aimed to evaluate the performance of the deep convolutional neural
network (DCNN) to discriminate between benign, borderline, and malignant serous ovarian …

Objective This study aimed to evaluate and validate the performance of deep convolutional
neural networks when discriminating different histologic types of ovarian tumor in ultrasound …

Objectives To develop and test the performance of computerized ultrasound image analysis
using deep neural networks (DNNs) in discriminating between benign and malignant …

Discrimination of ovarian tumors is necessary for proper treatment. In this study, we
developed a convolutional neural network model with a convolutional autoencoder (CNN …

Background Upon the discovery of ovarian cysts, obstetricians, gynecologists, and
ultrasound examiners must address the common clinical challenge of distinguishing …

Background Ultrasound is a critical non-invasive test for preoperative diagnosis of ovarian
cancer. Deep learning is making advances in image-recognition tasks; therefore, we aimed …

Background Deep learning (DL) algorithms could improve the classification of ovarian
tumors assessed with multimodal US. Purpose To develop DL algorithms for the automated …

The classification of tumours into benign and malignant continues to date to be a very
relevant and significant research topic in the cancer research domain. With the advent of …

Background To develop an artificial intelligence (AI) model with radiomics and deep
learning (DL) features extracted from CT images to distinguish benign from malignant …

Ovarian cancer is one of the most common gynecologic malignancies. Accurate
classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid …