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 …

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

Ovarian cysts are one of the most common gynecologic disorders encountered in clinical
practice. The pelvic computed tomography (CT) scan is a commonly employed examination …

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

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 …

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

In recent days, artificial intelligence (AI) is gaining worldwide popularity in several industries
among which healthcare is an important sector. AI is being used in healthcare to reduce …

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

Deep Learning models have shown tremendously impressive performance on image
classification tasks. In the medical imaging domain, progress has been made in obtaining …