Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review
NIR Yassin, S Omran, EMF El Houby… - Computer methods and …, 2018 - Elsevier
Background and objective The high incidence of breast cancer in women has increased
significantly in the recent years. Physician experience of diagnosing and detecting breast …
significantly in the recent years. Physician experience of diagnosing and detecting breast …
A brief survey on breast cancer diagnostic with deep learning schemes using multi-image modalities
Patients with breast cancer are prone to serious health-related complications with higher
mortality. The primary reason might be a misinterpretation of radiologists in recognizing …
mortality. The primary reason might be a misinterpretation of radiologists in recognizing …
Classification of breast cancer histopathological images using DenseNet and transfer learning
Breast cancer is one of the most common invading cancers in women. Analyzing breast
cancer is nontrivial and may lead to disagreements among experts. Although deep learning …
cancer is nontrivial and may lead to disagreements among experts. Although deep learning …
Fast discrete curvelet transform and modified PSO based improved evolutionary extreme learning machine for breast cancer detection
A significant research area in medical imaging analysis is digital mammography breast
cancer detection in the early stage. For breast mass classification into the benign or …
cancer detection in the early stage. For breast mass classification into the benign or …
Detection and classification of masses in mammographic images in a multi-kernel approach
SML de Lima, AG da Silva-Filho… - Computer methods and …, 2016 - Elsevier
Abstract Background and Objective According to the World Health Organization, breast
cancer is the main cause of cancer death among adult women in the world. Although breast …
cancer is the main cause of cancer death among adult women in the world. Although breast …
Mammographic classification based on XGBoost and DCNN with multi features
R Song, T Li, Y Wang - IEEE Access, 2020 - ieeexplore.ieee.org
The classification of benign and malignant masses in mammograms by Computer-Aided
Diagnosis (CAD) is one of the most difficult and important tasks in the development of CAD …
Diagnosis (CAD) is one of the most difficult and important tasks in the development of CAD …
An efficient approach for automated mass segmentation and classification in mammograms
Breast cancer is becoming a leading death of women all over the world; clinical experiments
demonstrate that early detection and accurate diagnosis can increase the potential of …
demonstrate that early detection and accurate diagnosis can increase the potential of …
Breast mass classification on mammograms using radial local ternary patterns
C Muramatsu, T Hara, T Endo, H Fujita - Computers in biology and …, 2016 - Elsevier
Textural features can be useful in differentiating between benign and malignant breast
lesions on mammograms. Unlike previous computerized schemes, which relied largely on …
lesions on mammograms. Unlike previous computerized schemes, which relied largely on …
Computational analysis of histological images from hematoxylin and eosin-stained oral epithelial dysplasia tissue sections
Oral epithelial dysplasia is a precancerous lesion that presents alterations in the shape and
size of cell nuclei and can be graded as mild, moderate and severe. The conventional …
size of cell nuclei and can be graded as mild, moderate and severe. The conventional …
Radiomics and artificial intelligence analysis with textural metrics extracted by contrast-enhanced mammography in the breast lesions classification
The aim of the study was to estimate the diagnostic accuracy of textural features extracted by
dual-energy contrast-enhanced mammography (CEM) images, by carrying out univariate …
dual-energy contrast-enhanced mammography (CEM) images, by carrying out univariate …