Mammography is a routine imaging technique used by radiologists to detect breast lesions, such as tumors and lumps. Precise lesion detection is critical for early treatment and diagnosis planning. Lesion detection and segmentation are still problemat...
This study investigates the efficacy of fractal-based global texture features for distinguishing between malignant and normal mammograms and assessing their potential for molecular subtype differentiation. Digital mammograms were analyzed using stand...
PURPOSE: Breast cancer remains a leading cause of mortality in women worldwide, with notable disparities in incidence and prognosis across regions. This systematic review explores the application of Deep Learning-based computer-aided diagnostic (CAD)...
BMC medical informatics and decision making
Oct 13, 2025
BACKGROUND: Breast cancer remains one of the leading causes of cancer-related deaths globally, affecting both women and men. This study aims to develop a novel deep learning (DL)-based architecture, the Breast Cancer Ensemble Convolutional Neural Net...
Full-field digital mammography (FFDM) is the most common imaging technique for breast cancer screening programs. Still, it is limited by noise from quantum effects, electronic issues, and X-ray scattering, affecting the image quality. Traditional den...
Breast cancer continues to be a global public health challenge. An early and precise diagnosis is crucial for improving prognosis and efficacy. While deep learning (DL) methods have shown promising advances in breast cancer classification from mammog...
INTRODUCTION: Breast cancer Mammography (MAM) screening was proven to improve survival worldwide. However, younger patients with higher breast density made MAM less effective in China. It is necessary to establish Chinese-specific effective screening...
BACKGROUND: Breast cancer is the most common cancer among women and a leading cause of mortality in Europe. Early detection through screening reduces mortality, yet participation in mammography-based programs remains suboptimal due to discomfort, rad...
PURPOSE: To develop and validate a deep learning-based model for automated evaluation of mammography phantom images, with the goal of improving inter-radiologist agreement and enhancing the efficiency of quality control within South Korea's national ...
PURPOSE: To investigate the effectiveness of an integrated deep-learning (DL) algorithm, the Mixture of Radiological Findings Specific Experts (MoRFSE), in breast cancer classification by imitating the diagnostic decision-making process of radiologis...
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