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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