Deep learning has shown great potentials for medical image analysis and computer-aided
diagnosis of some diseases such as MRI brain tumor segmentation, mammogram …

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

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

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 …

Background/Objectives: This study aims to create a strong binary classifier and evaluate the
performance of pre-trained convolutional neural networks (CNNs) to effectively distinguish …

The classification of ovarian cancer types is a very challenging process for physicians' eyes.
To solve this problem, this article proposes a new deep learner, which classifies ovarian …

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

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

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 …

Ovarian Tumour is one of the common generally occurring tumours in females. Transvaginal
(US) ultrasonography is used as a screening examination to identify the appearance of …