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Table 5 Classification results for various texture models

From: Comparative analysis of proficiencies of various textures and geometric features in breast mass classification using k-nearest neighbor

Features Fine k-NN Medium k-NN Cosine k-NN Cubic k-NN Weighted k-NN
Accuracy (%) Sensitivity (%) Specificity (%) Accuracy (%) Sensitivity (%) Specificity (%) Accuracy (%) Sensitivity (%) Specificity (%) Accuracy (%) Sensitivity (%) Specificity (%) Accuracy (%) Sensitivity (%) Specificity (%)
SGLCM 54.0 58.0 49.0 59.0 53.0 68.0 56.0 60.0 51.0 56.0 51.0 64.0 57.0 62.0 50.0
GLDS 53.0 60.0 44.0 47.0 46.0 48.0 61.0 59.0 63.0 48.0 48.0 48.0 54.0 67.0 36.0
FOS 61.0 61.0 62.0 63.0 58.0 68.0 62.0 68.0 55.0 63.0 62.0 65.0 63.0 67.0 57.0
SFM 75.0 82.0 66.0 67.0 69.0 65.0 67.0 68.0 67.0 70.0 72.0 66.0 75.0 83.0 64.0
LTEM 56.0 60.0 51.0 61.0 69.0 51.0 63.0 68.0 59.0 60.0 67.0 52.0 64.0 77.0 48.0
Fractal 75.0 82.0 66.0 60.0 76.0 41.0 55.0 65.0 43.0 61.0 77.0 42.0 71.0 86.0 53.0
FPS 54.0 55.0 52.0 53.0 59.0 46.0 56.0 60.0 51.0 54.0 59.0 48.0 54.0 64.0 42.0