Temporomandibular joint disorders (TMDs) affect a large portion of the global population and are a common source of chronic ...

Cells contain a wealth of information about health and disease, but extracting that data reliably from microscope images remains a major challenge. Many important differences between healthy and ...

A new UCLA Health Jonsson Comprehensive Cancer Center study suggests that the way immune cells are organized inside melanoma ...

Abstract: Early detection of skin cancer (SC) is paramount for effective treatment. Although convolutional neural networks (CNN) have facilitated automated learning of high-level features from ...

Abstract: Osteosarcoma is the most aggressive type of bone cancer often associated with high morbidity, which makes it challenging to diagnose and prognosticate. In this paper, we present a completely ...

Bone-protecting therapies are an important part of delivering optimal care in patients with prostate cancer who are treated with androgen-deprivation therapy. Rahul Parikh, MD, a professor of medicine ...

LUNG cancer classification using lightweight convolutional neural networks has demonstrated high diagnostic accuracy while reducing computational complexity, according to new research evaluating ...

Early assessment of breast cancer relapse can significantly impact survival rates and overall oncological outcomes, highlighting the need to use sophisticated diagnostic strategies in clinical trials.

ABSTRACT: Lung cancer stands as the preeminent cause of cancer-related mortality globally. Prompt and precise diagnosis, coupled with effective treatment, is imperative to reduce the fatality rates ...

The main aim of this work is to build a robust Convolutional Neural Network (CNN) algorithm that efficiently and quickly classifies bone scintigraphy images, by determining the presence or absence of ...