C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Uniqueness of medical data mining
Artificial Intelligence in Medicine
Wavelet transform methods for object detection and recovery
IEEE Transactions on Image Processing
Mining lung cancer patient data to assess healthcare resource utilization
Expert Systems with Applications: An International Journal
GUEST EDITORIAL: Intelligent data analysis in medicine-Recent advances
Artificial Intelligence in Medicine
Computer-aided detection and diagnosis of breast cancer with mammography: recent advances
IEEE Transactions on Information Technology in Biomedicine
A computer-aided detection system for clustered microcalcifications
Artificial Intelligence in Medicine
Image processing and machine learning for fully automated probabilistic evaluation of medical images
Computer Methods and Programs in Biomedicine
Evolutionary design of decision trees for medical application
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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Objectives: The presence of microcalcifications (MCs), clusters of tiny calcium deposits that appear as small bright spots in a mammogram, has been considered as a very important indicator for breast cancer diagnosis. Much research has been performed for developing computer-aided systems for the accurate identification of MCs, however, the computer-based automatic detection of MCs has been shown difficult because of the complicated nature of surrounding of breast tissue, the variation of MCs in shape, orientation, brightness and size. Methods and materials: This paper presents a new approach for the effective detection of MCs by incorporating a knowledge-discovery mechanism in the genetic algorithm (GA). In the proposed approach, called knowledge-discovery incorporated genetic algorithm (KD-GA), the genetic algorithm is used to search for the bright spots in mammogram and a knowledge-discovery mechanism is integrated to improve the performance of the GA. The function of the knowledge-discovery mechanism includes evaluating the possibility of a bright spot being a true MC, and adaptively adjusting the associated fitness values. The adjustment of fitness is to indirectly guide the GA to extract the true MCs and eliminate the false MCs (FMCs) accordingly. Results and conclusions: The experimental results demonstrate that the incorporation of knowledge-discovery mechanism into the genetic algorithm is able to eliminate the FMCs and produce improved performance comparing with the conventional GA methods. Furthermore, the experimental results show that the proposed KD-GA method provides a promising and generic approach for the development of computer-aided diagnosis for breast cancer.