Table 2 Ablation study on ILN-GNet for PD Detection using Dataset 1.

From: Automated detection of Parkinson’s disease using improved linknet-ghostnet model based on handwriting images

Metrics

ILN-GNet

Model with conventional LinkNet

Model with conventional LinkNet and Ghostnet

Model with conventional PHOG

Model with conventional WF

Model without Feature Extraction

Model without MDSCM

Model without WAP-BN

Accuracy

96.40%

93.10%

95.20%

90.30%

86.30%

92.30%

89.30%

90.80%

Sensitivity

95.70%

92.20%

95.70%

89.60%

84.30%

93.90%

89.10%

91.50%

Specificity

97.00%

94.00%

94.70%

91.00%

88.00%

91.00%

89.50%

90.20%

Precision

96.50%

93.00%

94.00%

89.60%

85.80%

90.00%

87.90%

89.00%

F-measure

96.10%

92.60%

94.80%

89.60%

85.10%

91.90%

88.50%

90.20%

MCC

92.70%

86.20%

90.30%

80.50%

72.40%

84.70%

78.60%

81.60%

NPV

96.30%

93.30%

96.20%

91.00%

86.70%

94.50%

90.60%

92.60%

FPR

3.00%

6.00%

5.30%

9.00%

12.00%

9.00%

10.50%

9.80%

FNR

4.30%

7.80%

4.30%

10.40%

15.70%

6.10%

10.90%

8.50%

FDR

3.50%

7.00%

6.00%

10.40%

14.20%

10.00%

12.10%

11.00%