Skip to main content

Table 4 Results of nodule classification at wavelet second decomposition level

From: Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine

Base

Angle

Sub-band

Features

Specificity

Sensitivity

Preciseness

Db1

HH2

Clpr-Ener

68.18%

68.18%

71.11%

Db2

LL

Autc-Sent

36.36%

91.30%

64.44%

Db4

LL

Autc-Ent

36.36%

95.45%

65.90%

Db1

45°

LL

Clsh-Ener

82.60%

68.18%

75.55%

Db2

45°

LL

Autc-Ener

73.91%

63.63%

68.88%

Db4

45°

LL

Autc-Sent

82.6%

59.09%

71.11%

Db1

90°

LL

Autc-Ener

86.95%

50%

68.88%

Db2

90°

LL

Autc-Ent

47.82%

95.45%

71.11%

Db4

90°

LL

Autc-Ent

54.16%

71.42%

62.22%

Db1

135°

LL

Autc-Ener

65.21%

63.63%

64.44%

Db2

135°

LL

Autc-Ent

60.86%

77.27%

68.88%

Db4

135°

HH

Autc-Sent

90.90%

56.21%

73.33%

  1. The bold data represent the best value obtained.