Adaptive classification model based on artificial immune system for breast cancer detection

G Magna,S Velappa Jayaraman,Paola Casti,Arianna Mencattini,C Di Natale,E Martinelli

Trento（2015）

引用 9|浏览14

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摘要

Early stage asymmetric signs in breast that can be captured by the screening-digital mammography can be used for a precocious diagnosis of breast cancer. Conventional mammography screening fails to detect subtle anomalies, so computer-aided methods are studied in order to improve the accuracy of image analysis. To classify the images into asymmetric and normal cases, in this paper we investigated the performance of an Adaptive Artificial Immune System (A2INET) classifier. To test the efficiency of the algorithm, two public datasets have been considered: 32 pairs of mammographic images including MLO projection retrieved from Digital Database for Screening mammographic (DDSM) and 30 ones from Mammographic Image Analysis Society (mini-MIAS) databases. Results show that A2INET yields best results with respect to the other more conventional classifiers.

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关键词

artificial immune systems,biological organs,cancer,image classification,image retrieval,mammography,medical image processing,a2inet classifier,ddsm,mlo projection retrieval,mammographic image analysis society databases,adaptive artificial immune system classifier,adaptive classification model,asymmetric cases,breast cancer detection,computer-aided methods,digital database-for-screening mammography,mammographic images,minimias databases,precocious breast cancer diagnosis,screening-digital mammography,adaptive artificial immune system,mlo projection,breast cancer,screen-digital mammography,feature extraction,delta sigma modulation,immune system,adaptive systems,databases

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