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Preliminary development of anatomically realistic breast tumor models for microwave imaging
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Resumo(s)
In this work, we present a preliminary study of three classifiers – Linear Discriminant Analysis (LDA), Support Vector Machines (SVMs) and k-Nearest Neighbors (kNN) – to differentiate between malignant and benign tumors extracted from Magnetic Resonance (MR) images, based on their morphological features. The dataset in this study comprises 24 tumors: 12 malignant and 12 benign. Twelve morphological features were initially considered for tumor classification. The Mann-Whitney test was employed for feature selection, and the performance of the classifiers was evaluated with accuracy, sensitivity, specificity, F1-score and Matthew’s Correlation Coefficient (MCC) metrics. kNN (with k=6 and Chebyshev distance) outperformed the other classifiers with an accuracy, sensitivity, and specificity of 87.5%, 83.3% and 91.7%, respectively.
Descrição
Palavras-chave
breast cancer tumor classification morphological features