{"title":"Cost Efficient Mammogram Segmentation and Classification with NeuroMem® Chip for Breast Cancer Detection","authors":"Soumeya Demil, Lydia Bouzar-Benlabiod, G. Paillet","doi":"10.1109/IRI58017.2023.00054","DOIUrl":null,"url":null,"abstract":"In this paper, a Computer Aided Diagnosis system to detect and classify anomalies on mammograms is proposed. A segmentation method for anomaly extraction has been proposed using the NeuroMem® Chip NM500 which integrates physical neural networks, up to 83% of the anomalies were detected. We configured two subnetworks for the mammogram classification step the accuracy reached 87%.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRI58017.2023.00054","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
In this paper, a Computer Aided Diagnosis system to detect and classify anomalies on mammograms is proposed. A segmentation method for anomaly extraction has been proposed using the NeuroMem® Chip NM500 which integrates physical neural networks, up to 83% of the anomalies were detected. We configured two subnetworks for the mammogram classification step the accuracy reached 87%.
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