Abstract
Breast cancer is one of the major causes of death among women all over the world. Presently, mammographic analysis is the most used method for early detection of abnormalities. This paper presents a computational methodology to help the specialist with this task. In the first step, the K-Means clustering algorithm and the Template Matching technique are used to detect suspicious regions. Next, the texture of each region is described using the Simpson’s Diversity Index, which is used in Ecology to measure the biodiversity of an ecosystem. Finally, the information of texture is used by SVM to classify the suspicious regions into two classes: masses and non-masses. The tests demonstrate that the methodology has 79.12% of accuracy, 77.27% of sensitivity, and 79.66% of specificity.
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Nunes, A.P., Silva, A.C., de Paiva, A.C. (2009). Detection of Masses in Mammographic Images Using Simpson’s Diversity Index in Circular Regions and SVM. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_41
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DOI: https://doi.org/10.1007/978-3-642-03070-3_41
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