Abstract
A mortal disease affecting women globally is breast cancer. The survival rate rises in the matter of breast cancer when the presence of mass is identified ahead of time through a mammogram. The mass is obscured in dense breast tissues and hence the sensitivity is limited in the case of mammography. A computer aided diagnosis (CAD) system helps in overcoming the sensitivity issue in mammography but in turn the system is prone to many false positives. The proposed work incorporates the development of automated density-specific models for false positive reduction. A feature-classifier combination that performs the false positive reduction efficiently in each expert model has been identified. Normal and abnormal mammograms of all the four density types from the Image Retrieval in Medical Applications (IRMA) version of the Digital Database for Screening Mammography (DDSM) database have been employed in this work, resulting in the classification accuracy of 96%, 80%, 76% and 88% and false positive rates (FPR) of 0, 0.26, 0.25 and 0.12 for each density-specific mass detection models respectively.
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Shrinithi, S., Lavanya, R., Vijayan, D. (2023). False Positive Reduction in Mammographic Mass Detection. In: Mercier-Laurent, E., Fernando, X., Chandrabose, A. (eds) Computer, Communication, and Signal Processing. AI, Knowledge Engineering and IoT for Smart Systems. ICCCSP 2023. IFIP Advances in Information and Communication Technology, vol 670. Springer, Cham. https://doi.org/10.1007/978-3-031-39811-7_5
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DOI: https://doi.org/10.1007/978-3-031-39811-7_5
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