Table 4 The overall performance of the algorithms.

From: A bio-inspired convolution neural network architecture for automatic breast cancer detection and classification using RNA-Seq gene expression data

Methods

MVO-CNN

GA-CNN

SBO-CNN

WOA-CNN

CNN

LCBO-CNN

EOSA-CNN

Balanced accuracy

0.923

0.943

0.943

0.957

0.925

0.941

0.959

Accuracy

0.977

0.983

0.983

0.980

0.980

0.980

0.983

Precision

0.889

0.926

0.926

0.867

0.923

0.893

0.897

Recall

0.857

0.893

0.893

0.929

0.857

0.893

0.929

f1-scores

0.873

0.909

0.909

0.897

0.889

0.893

0.912

Cohens kappa

0.860

0.900

0.900

0.886

0.878

0.882

0.903

Sensitivity

0.989

0.993

0.993

0.985

0.993

0.989

0.989

Specificity

0.857

0.893

0.893

0.929

0.857

0.893

0.929