Table 10 Error assessment of the LMIBCD-DL approach with existing models under the breakhis dataset.

From: Leveraging medical imaging and deep learning for diagnosis of breast cancer using histopathological images

Approaches

\(\:\varvec{A}\varvec{c}\varvec{c}\varvec{u}{\varvec{r}}_{\varvec{y}}\)

\(\:\varvec{S}\varvec{e}\varvec{n}{\varvec{s}}_{\varvec{y}}\)

\(\:\varvec{S}\varvec{p}\varvec{e}{\varvec{c}}_{\varvec{y}}\)

\(\:\varvec{F}{1}_{\varvec{S}\varvec{c}\varvec{o}\varvec{r}\varvec{e}}\)

LMIBCD-DLHA

1.28

1.28

1.28

1.36

ResNet-LSTM

5.83

5.46

7.75

18.59

Vit-L16

17.54

15.40

3.56

18.69

BCHI-CovNet

10.38

18.60

11.03

4.26

AOADL-HBCC

3.18

17.91

4.83

18.80

DTLRO-HCBC

6.40

6.06

8.33

19.17

InceptionV3

18.26

15.93

4.32

19.25

MobileNetV3

10.93

19.11

11.61

4.93

ImageNet + VGG16 (IVNet)

2.95

17.10

7.49

14.31

VGG16 Model

19.80

14.79

12.84

17.21

ResNet-50 Model

17.77

3.17

5.34

4.73