Abstract
Several computer-aided testing tools have been developed more than ever in breast cancer research to minimize misdiagnosis. In this paper, a data mining method has been discussed that could help oncologists identify and detect breast cancer. Including microcalcifications, masses, and even regular findings from tissue was used as a stable database of 410 images. Two extraction techniques have been applied, particularly the unit for gray standard and length of the gray level unit. Many data mining classifications were also used for classification purposes. The findings were shown to be very favorable (roughly 70%) in terms of mammogram separation and BI-RADS® scale (>75%) with acceptable reliability and functional precision. The classification of random forest was the best predictive method to distinguish microcalcification with excellent performance.
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Bhowmik, C., Pradeep Ghantasala, G.S., AnuRadha, R. (2021). A Comparison of Various Data Mining Algorithms to Distinguish Mammogram Calcification Using Computer-Aided Testing Tools. In: Goyal, D., Gupta, A.K., Piuri, V., Ganzha, M., Paprzycki, M. (eds) Proceedings of the Second International Conference on Information Management and Machine Intelligence. Lecture Notes in Networks and Systems, vol 166. Springer, Singapore. https://doi.org/10.1007/978-981-15-9689-6_58
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