Abstract
Female breast cancer is a major cause of death in western countries. Several computer techniques have been developed to aid radiologists to improve their performance in the detection and diagnosis of breast abnormalities. In Point Pattern Analysis, there is a statistic known as Ripley’s K function that is frequently applied to Spatial Analysis in Ecology, like mapping specimens of plants. This paper proposes a new way in applying Ripley’s K function to classify breast masses from mammogram images. The features of each nodule image are obtained through the calculate of that function. Then, the samples gotten are classified through a Support Vector Machine (SVM) as benign or malignant masses. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. The best result achieved was 94.94% of accuracy, 92.86% of sensitvity and 93.33% of specificity.
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de Oliveira Martins, L., da Silva, E.C., Silva, A.C., de Paiva, A.C., Gattass, M. (2007). Classification of Breast Masses in Mammogram Images Using Ripley’s K Function and Support Vector Machine. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_59
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DOI: https://doi.org/10.1007/978-3-540-73499-4_59
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