AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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It is suggested that density-associated thermal patterns may be detectable and motivate further investigation of thermal imaging as a complementary modality for breast tissue characterization in larger and independently validated cohorts.
INTRODUCTION Average glandular dose (Dg) is the primary metric for assessing radiation risk in screening mammography. Although Dg analysis is commonly interpreted according to compressed breast thickness (CBT), breast density (BD) may significantly influence Dg. This study evaluated the influence of BD on Dg variabilit...
Breast cancer (BC) remains one of the foremost causes of cancer-related mortality among women globally, and early detection through mammography screening is essential for improving patient survival outcomes. Although recent deep learning frameworks have demonstrated encouraging performance in automated mammogram classi...
A systematic but innovative two phases transfer learning’sbased deep learning classification framework is developed using popular EfficientNetB7 architecture architecture for breast cancer classification which encompasses formalisation into components like data augmentation, progressive fine-tuning and adaptive learnin...
BACKGROUND Breast density is a critical factor in both breast cancer risk and the effectiveness of diagnostic technologies. Women with dense breast tissue not only have a higher risk of developing breast cancer but also face challenges in screening and diagnosis using mammography. Microwave-based breast evaluation is a...
Breast cancer continues to be the most common cause of cancer-related deaths in women globally. The earlier breast cancer is identified, the higher the chance of survival. In screening dense breast tissue, the standard diagnostic approach has essential limitations, including a relatively high false-negative rate and po...
A deep learning model for breast ultrasound image analysis to improve lesion segmentation and support computer-aided breast cancer diagnosis and integrates morphological component analysis, convolutional learning, and explainable artificial intelligence into a unified model for breast ultrasound lesion segmentation.
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