Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …

Deep neural networks have demonstrated promising performance in screening
mammography with recent studies reporting performance at or above the level of trained …

Artificial intelligence (AI) applications in mammography have gained significant popular
attention; however, AI has the potential to revolutionize other aspects of breast imaging …

Prostate cancer is the most common cancer in men worldwide and the second leading
cause of cancer death in the United States. One of the prognostic features in prostate cancer …

Breast cancer (BC) is the leading cause of mortality among women across the world. Earlier
screening of BC can significantly reduce the mortality rate and assist the diagnostic process …

Objectives Siamese neural networks (SNN) were used to classify the presence of
radiopaque beads as part of a colonic transit time study (CTS). The SNN output was then …

Background Radiomic features and deep features are both vitally helpful for the accurate
prediction of tumor information in breast ultrasound. However, whether integrating radiomic …

Mammogram image analysis has benefited from advancements in artificial intelligence (AI),
particularly through the use of Siamese networks, which, similar to radiologists, compare …

Fast growing global connectivity and urbanisation increases the risk of spreading worldwide
disease. The worldwide SARS-COV-2 disease causes healthcare system strained …

Background This research aims to use deep learning to create automated systems for better
breast cancer detection and categorisation in mammogram images, hel** medical …