Fully automated scheme for computer-aided detection and breast cancer diagnosis using digitised mammograms - Eltrass, Ahmed S; Salama, Mohamed S (IET image processing, 2020)
Keywords: benign–malignant classification; benign–malignant patterns; breast cance early detection; breast cancer diagnosis; CAD system; cancer; computer‐aided detection; correlation bias reduction algorithm; correlation methods; digital mammograms; expectation‐maximisation algorithm; feature extraction; feature extraction technique; feature selection; fully automated computer‐aided diagnosis system; hybrid feature selection approach; image classification; image resolution; image texture; learning (artificial intelligence); low‐computational requirements; mammography; medical image processing; multiorientation features; multiresolution features; object detection; public health problem; Q similarity‐based learning algorithm; real clinical mammograms; real‐time systems; region of interest extraction; Research Article; ROIs; statistical analysis; support vector machine recursive feature elimination; support vector machines; textural features; time 2.2 s; wavelet transforms; wavelet‐based contourlet transform; digital mammograms; benign–malignant classification; wavelet transforms; image classification; multiorientation features; breast cancer diagnosis; fully automated computer-aided diagnosis system; time 2.2 s; object detection; computer-aided detection; region of interest extraction; multiresolution features; feature extraction technique; feature extraction; wavelet-based contourlet transform; Q similarity-based learning algorithm; learning (artificial intelligence); real clinical mammograms; medical image processing; image resolution; feature selection; ROIs; support vector machines; mammography; CAD system; hybrid feature selection approach; correlation bias reduction algorithm; image texture; support vector machine recursive feature elimination; expectation–maximisation algorithm; low-computational requirements; benign–malignant patterns; textural features; public health problem; real-time systems; expectation-maximisation algorithm; cancer; statistical analysis; breast cance early detection; correlation methods