Benign and malignant breast cancer segmentation using optimized region growing technique
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K. Suresh Joseph | S. Punitha | A. Amuthan | S. Punitha | K. S. Joseph | A. Amuthan | K. Suresh Joseph
[1] Yudy Purnama,et al. Mammogram Classification using Law's Texture Energy Measure and Neural Networks , 2015 .
[2] Heng-Da Cheng,et al. Approaches for automated detection and classification of masses in mammograms , 2006, Pattern Recognit..
[3] Zhigang Zeng,et al. A new automatic mass detection method for breast cancer with false positive reduction , 2015, Neurocomputing.
[4] Brijesh Verma,et al. A novel soft cluster neural network for the classification of suspicious areas in digital mammograms , 2009, Pattern Recognit..
[5] Arturo J. Méndez,et al. Computerized detection of breast masses in digitized mammograms , 2007, Comput. Biol. Medicine.
[6] Yunsong Li,et al. Breast mass classification in digital mammography based on extreme learning machine , 2016, Neurocomputing.
[7] Ge Yu,et al. Breast tumor detection in digital mammography based on extreme learning machine , 2014, Neurocomputing.
[8] Arianna Mencattini,et al. Identification of mammography anomalies for breast cancer detection by an ensemble of classification models based on artificial immune system , 2016, Knowl. Based Syst..
[9] A. Vadivel,et al. A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories , 2013, Comput. Biol. Medicine.
[10] C. Metz. Basic principles of ROC analysis. , 1978, Seminars in nuclear medicine.
[11] A. Rényi. On Measures of Entropy and Information , 1961 .
[12] J. Hazel,et al. BINARY (PRESENCE-ABSENCE) SIMILARITY COEFFICIENTS , 1969 .
[13] Saroj Kumar Lenka,et al. Texture-based features for classification of mammograms using decision tree , 2012, Neural Computing and Applications.
[14] Qaisar Abbas,et al. Breast mass segmentation using region-based and edge-based methods in a 4-stage multiscale system , 2013, Biomed. Signal Process. Control..
[15] Sasikala Jayaraman,et al. Self-adaptive dragonfly based optimal thresholding for multilevel segmentation of digital images , 2016, J. King Saud Univ. Comput. Inf. Sci..
[16] Manish Kumar Bajpai,et al. Breast cancer detection in digital mammograms , 2015, 2015 IEEE International Conference on Imaging Systems and Techniques (IST).
[17] Sanjay N. Talbar,et al. Genetic Fuzzy System (GFS) based wavelet co-occurrence feature selection in mammogram classification for breast cancer diagnosis☆ , 2016 .
[18] Brijesh Verma,et al. A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms , 2007, Appl. Soft Comput..
[19] Wei Qian,et al. An improved method of region grouping for microcalcification detection in digital mammograms. , 2002 .
[20] J. Dheeba,et al. A Swarm Optimized Neural Network System for Classification of Microcalcification in Mammograms , 2012, Journal of Medical Systems.
[21] Arnau Oliver,et al. Topological Modeling and Classification of Mammographic Microcalcification Clusters , 2015, IEEE Transactions on Biomedical Engineering.
[22] Martin Kom,et al. Automated detection of masses in mammograms by local adaptive thresholding , 2007, Comput. Biol. Medicine.
[24] Shohreh Kasaei,et al. Benign and malignant breast tumors classification based on region growing and CNN segmentation , 2015, Expert Syst. Appl..
[25] Seyedali Mirjalili,et al. Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems , 2015, Neural Computing and Applications.
[26] Marcelo Zanchetta do Nascimento,et al. Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm , 2014, Comput. Methods Programs Biomed..