Signal and image restoration using shock filters and anisotropic diffusion
SIAM Journal on Numerical Analysis
Approaches for automated detection and classification of masses in mammograms
Pattern Recognition
Shock filter coupled to curvature diffusion for image denoising and sharpening
Image and Vision Computing
Artificial Intelligence in Medicine
Computer-aided detection and diagnosis of breast cancer with mammography: recent advances
IEEE Transactions on Information Technology in Biomedicine
Multiobjective GAs, quantitative indices, and pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
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An enhanced Computer Aided Clinical Decision Making System using Multiple classifier systems (MCSs) based on the combination of a set of different classifiers for classifying the breast tumor as malignant and benign has been developed and presented in this paper. The Multilayer Back Propagation Neural Network (MBPN), Radial-Basis-Function Neural Network (RBFNN), Asymmetrical Support Vector Machine (ASVM) and combined classifier with major voting method, behaviour-knowledge space method have been used to classify the tumor. The multiple features with optimal feature selection and combined classifier with behaviour-knowledge space method is found to have the accuracy 99.97%. The performance of the proposed clinical decision support system has been estimated and found that this hybrid system will provide valuable information to the physicians in clinical pathology.