Tables And Figures
Table 1.
Accuracy obtained for 16 sample images. Mean accuracy 94.59 % for morphological algorithm and
93.61 % for Genetic algorithm
Morphological (Group-1)
Genetic (Group-2)
94.59
93.61
93.25
91.85
92.38
90.34
91.24
88.67
90.56
87.16
89.73
85.63
88.38
83.27
87.53
81.84
86.62
80.58
85.33
79.52
84.72
79.15
84.11
78.71
83.86
78.53
83.34
78.23
Journal of Pharmaceutical Negative Results ¦Volume13¦SpecialIssue 4¦2022
275
Y. Rakesh
,
etal
.:
Bone Fracture Detection Using Morphological and Comparing the Accuracy with Genetic Algorithm
82.47
77.74
81.36
77.23
Table 2.
Represents group statistics for both sample groups, Mean ( 87.4669 ), standard deviation (4.11895 &
5.51614) and standard error mean (1.02974 & 1.37904).
Group Statistics
Group
N
Mean
Std.Deviation
Std.error
Morphological
16
87.4669
4.11895
1.02974
Genetic
16
83.2538
5.51614
1.37904
Table 3.
Represents statistical analysis of independent sample tests for both sample groups. T value ( 2.448 ), df
value ( 30 & 27.760 ) with mean difference 4.21312, significance P-value ( 0.029 )
Lavene’s
test for
equality of
variances
T-test for Equality of Means
95% confidence
interval of the
difference
F
Sig
t
df
sig (2
tailed)
Mean
diff
Std.
error
Lower
Upper
Accuracy
Equal
Variances
assumed
2.27
.029
2.448
30
0.20
4.21312 1.72108
.69822
7.72803
Equal
Variances
not
assumed
2.448 27.760
0.21
4.21312 1.72108
.68629
7.73996
Journal of Pharmaceutical Negative Results ¦Volume13¦SpecialIssue 4¦2022
276
Y. Rakesh
,
etal
.:
Bone Fracture Detection Using Morphological and Comparing the Accuracy with Genetic Algorithm
Fig. 1.
Represents MATLAB simulation Opened and Closed images obtained by the Morphological algorithm
input image with contrast stretched.
Fig. 2.
Comparison analysis of mean accuracy for two groups using Genetic and morphological. morphological
shows better accuracy compared with Genetic with error bar 95%, parameter shows statistically significant p-
value=0.05. Mean accuracy of detection=+/- 1SD.
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