Benign and malignant breast tumors classification based on region growing and CNN segmentation
作者：
Highlights：
• CNN templates are generated using a genetic algorithm to segment mammograms.
• An adaptive threshold is computed in region growing process by using ANN and intensity features.
• In tumor classification, CNN produces better results than region growing.
• MLP produces the highest classification accuracy among other classifiers.
• Results on DDSM images are more appropriate than those of MIAS.
摘要
•CNN templates are generated using a genetic algorithm to segment mammograms.•An adaptive threshold is computed in region growing process by using ANN and intensity features.•In tumor classification, CNN produces better results than region growing.•MLP produces the highest classification accuracy among other classifiers.•Results on DDSM images are more appropriate than those of MIAS.
论文关键词：Breast cancer,Segmentation,Cellular neural network,Region growing,Genetic algorithm,Artificial neural network
论文评审过程：Available online 27 September 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.09.020
