Table 4 Comparison analysis of the COADL-MNSC technique with other models11.

From: Transferable deep learning with coati optimization algorithm based mitotic nuclei segmentation and classification model

Methods

\(Acc{u}_{y}\)

\(Pre{c}_{n}\)

\(Rec{a}_{l}\)

\({F}_{score}\)

COADL-MNSC

98.89

98.94

98.86

98.89

MNSC-CBOADL

98.42

98.35

98.40

98.36

Xception-BOA-DBN

97.14

97.48

97.10

97.17

Xception-DBN

96.91

96.94

96.86

96.87

AHBATL-MNC

96.79

96.80

96.79

96.69

DHE-Mit

85.25

84.48

75.29

77.35

DenseNet-201

83.99

83.22

73.86

76.40

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