Abstract
Although, there are various forms of medical image processing techniques for identification of breast cancer, but majority of the technique are either expensive, or time dependent, or doesn’t produce accurate outcomes. We reviewed the recent techniques of breast cancer detection to find that the sole conclusion of presence or absence of disease depends on the skills of a radiologist. The present manuscript introduces a very simple modeling of breast cancer detection followed by multiple level of optimization carried out towards its extracted feature. A transform-based technique is used for feature extraction which is further optimized using particle swarm optimization for precise detection of cancerous tissues within a mammogram. Finally, we use a set of simplified fuzzy rules in order to identify the type of the cancer. The presented system offers faster response time with negligible computational complexity.
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Sushma, S.J., Prasanna Kumar, S.C. (2017). Multi-stage Optimization Over Extracted Feature for Detection and Classification of Breast Cancer. In: Silhavy, R., Silhavy, P., Prokopova, Z., Senkerik, R., Kominkova Oplatkova, Z. (eds) Software Engineering Trends and Techniques in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 575. Springer, Cham. https://doi.org/10.1007/978-3-319-57141-6_29
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DOI: https://doi.org/10.1007/978-3-319-57141-6_29
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