Segmentation for mammography classification utilizing deep convolutional neural network.

Background: Mammography for the diagnosis of early breast cancer (BC) relies heavily on the identification of breast masses. However, in the early stages, it might be challenging to ascertain whether a breast mass is benign or malignant. Consequently, many deep learning (DL)-based computer-aided diagnosis (CAD) approaches for BC classification have been developed.

Does Discussing Patient-reported Outcome Measures Increase Pain Self-efficacy at an Orthopaedic Visit? A Prospective, Sequential, Comparative Series.

Background: Pain self-efficacy, or the ability to carry out desired activities in the presence of pain, can affect a patient's ability to function before and after orthopaedic surgery. Previous studies suggest that shared decision-making practices such as discussing patient-reported outcome measures (PROMs) can activate patients and improve their pain self-efficacy. However, the ability of PROMs to influence pain self-efficacy in patients who have undergone orthopaedic surgery has yet to be investigated.

Effect of Acetabular Labral Tear Orientation on Hip Joint Kinematics: A Comparison of Radial Tears, Chondrolabral Junction Tears and Complex Tears in Cadaveric Hips.

Background: Acetabular labral tear morphology or orientation may influence hip stability.

Hypothesis: A radial tear of the acetabular labrum would result in greater rotational and translational motion compared with a chondrolabral separation.

Study Design: Controlled laboratory study.

Fusing global context with multiscale context for enhanced breast cancer classification.

Breast cancer is the second most common type of cancer among women. Prompt detection of breast cancer can impede its advancement to more advanced phases, thereby elevating the probability of favorable treatment consequences. Histopathological images are commonly used for breast cancer classification due to their detailed cellular information.

Ensemble of hybrid model based technique for early detecting of depression based on SVM and neural networks.

The prevalence of depression has increased dramatically over the last several decades: it is frequently overlooked and can have a significant impact on both physical and mental health. Therefore, it is crucial to develop an automated detection system that can instantly identify whether a person is depressed. Currently, machine learning (ML) and artificial neural networks (ANNs) are among the most promising approaches for developing automated computer-based systems to predict several mental health issues, such as depression.