Deep learning approach for skin melanoma and benign classification using empirical wavelet decomposition.

Background: Melanoma is a malignant skin cancer that causes high mortality. Early detection of melanoma can save patients' lives. The features of the skin lesion images can be extracted using computer techniques to differentiate early between melanoma and benign skin lesions.

Cross-leg flaps: Case series, review, and proposed classification.

Background: Cross-leg (CL) flap procedures have a long history in reconstructive surgery, having been described for the first time in 1854. The application of these flaps can potentially solve many reconstructive issues with satisfactory outcomes.

Patients And Methods: During our research into the history and development of CL flaps, we identified a variety of flaps for which a classification system can be proposed based on blood supply and flap modifications.

Repair of flexor digitorum profundus avulsions including the palmar plate: a retrospective comparative study of 56 cases.

We retrospectively reviewed 56 fingers with a type 1 flexor digitorum profundus avulsion (jersey finger) injury. Nineteen fingers were treated with a four-strand pull-out suture technique that did not include the distal palmar plate, and 37 were treated with a six-strand pull-out suture technique that included the distal palmar plate. An early active mobilization regimen was used.

Mathematical and deep learning analysis based on tissue dielectric properties at low frequencies predict outcome in human breast cancer.

Background: The early detection of human breast cancer represents a great chance of survival. Malignant tissues have more water content and higher electrolytes concentration while they have lower fat content than the normal. These cancer biochemical characters provide malignant tissue with high electric permittivity (ε´) and conductivity (σ).