Classification of Breast Cancer Molecular Subtypes from Their Micro-Texture in Mammograms Using a VGGNet-Based Convolutional Neural Network.
Vivek Kumar SinghSantiago RomaniJordina Torrents-BarrenaFarhan AkramNidhi PandeyMd. Mostafa Kamal SarkerAdel SalehMeritxell ArenasMiguel ArquezDomenec PuigPublished in: CCIA (2017)
Keyphrases
- breast cancer
- convolutional neural network
- breast cancer diagnosis
- clustered microcalcifications
- mammogram images
- bladder cancer
- roc analysis
- benign and malignant
- computer aided detection
- diagnosis of breast cancer
- microcalcification clusters
- computer aided diagnosis
- breast tissue
- logistic regression
- early detection
- cancer datasets
- early detection of breast cancer
- breast cancer patients
- decision trees
- face detection
- classification accuracy
- survival analysis
- feature extraction
- text classification
- support vector
- cancer patients
- mammographic images
- feature selection
- multi class
- digital mammograms
- dce mri
- lung nodules
- textural features
- outcome prediction
- texture features
- digitized mammograms
- gray level
- breast cancer detection
- feature set
- feature vectors
- feature space
- machine learning