Background Accurate identification of ovarian cancer (OC) is of paramount importance in
clinical treatment success. Artificial intelligence (AI) is a potentially reliable assistant for the …

Since surface defect detection is significant to ensure the utility, integrality, and security of
productions, and it has become a key issue to control the quality of industrial products, which …

Objectives Different noninvasive imaging methods to predict the chance of malignancy of
ovarian tumors are available. However, their predictive value is limited due to subjectivity of …

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

Ovarian cancer is one of the most harmful gynecological diseases. Detecting ovarian tumors
in early stage with computer-aided techniques can efficiently decrease the mortality rate …

Simple Summary According to cancer research statistics, there are 7500 new ovarian cancer
diagnoses in the UK each year. An earlier detection of ovarian cancer leads to a better …

Wrist trauma is common in children and generally requires radiography for exclusion of
fractures, subjecting children to radiation and long wait times in the emergency department …

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 …

Ovarian cancer poses a major worldwide health issue, marked by high death rates and a
deficiency in reliable diagnostic methods. The precise and prompt detection of ovarian …

Ultrasound-based models exist to support the classification of adnexal masses but are
subjective and rely upon ultrasound expertise. We aimed to develop an end-to-end machine …