Table 9 The efficacy of the suggested approach was compared with an earlier study that employed segmentation techniques to improve the accuracy of mammography class prediction on mammography datasets

Garrucho et al. [39]

OPTIMAM

No

Detection

Defor-DETR

0.948

Li et al. [31]

DDSM

No

Classification

TV-NN

0.968

Al-masni et al. [32]

DDSM

No

Detection and Classification

CNN

0.97

Ragab et al. [23]

DDSM

Yes

Detection

DCNN-SVM-AlexNet

0.88

Salama et al. [24]

DDSM

Yes

Classification

InceptionV3

0.989

Khamparia et al. [38]

DDSM

Yes

Detection

Hybrid MVGG16 ImageNet

0.933

Das et al. [33]

INbreast

No

Detection

Xception

N\(\backslash\)A

Saffari et al. [34]

INbreast

Yes

Classification

CNN

N\(\backslash\)A

Hama et al. [35]

INbreast

Yes

Detection and Classification

DenseNet121

N\(\backslash\)A

Proposed Model

INbreast

Yes

Prediction and Classification

Pyramid Transformer

0.998