Deep Learning-Based Automated Reports for Breast Density Assessment in Mammography Images.

A novel hierarchical transfer learning to enhance breast cancer detection on screening mammography using deep learning.

Comparative Heatmap Explanations for Deep Learning-Based Breast Density Classification

Breast density is associated with a higher risk of developing breast cancer and complicates mammographic interpretation because dense tissue can obscure suspicious findings. Deep learning (DL) models are increasingly used for automated breast density classification, yet their limited interpretability remains a concern...

DensiThAI: A Multi-View Deep Learning Framework for Breast Density Estimation Using Infrared Images.

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.

Prediction Modeling for Breast Cancer Treatment Planning Using Contrast-Enhanced Mammography

It is suggested that integrating advanced CNN architectures with radiomics features can significantly enhance the accuracy and reliability of both lesion segmentation and treatment prediction in breast cancer, potentially leading to better clinical outcomes.

Hybrid Spatial and Frequency Feature Learning for Breast Cancer Analysis

Breast cancer is one of the leading causes of cancer-related mortality among women worldwide, where early diagnosis significantly improves treatment outcomes and survival rates. Full-field digital mammography (FFDM) is widely employed for breast cancer screening; however, accurate classification of mammographic abnorma...

Automated Breast Cancer Detection Using GLCM Texture Analysis and Ensemble Learning Techniques

The findings demonstrate that integrating hand-crafted textural features and boosting techniques can mitigate false negative risks, offering a highly efficient and comprehensible instrument for clinical decision support in the screening of breast cancer.