Table 5 BT detection performance of the proposed EBTS-BIFMFBO model on 70%TRPH and 30%TSPH datasets. Evaluation includes \(\:acc{u}_{y}\), \(\:pre{c}_{n}\), \(\:rec{a}_{l}\), \(\:{F1}_{score}\), and \(\:MCC\).

From: Deep transfer learning based feature fusion model with Bonobo optimization algorithm for enhanced brain tumor segmentation and classification through biomedical imaging

Classes

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

\(\:\varvec{P}\varvec{r}\varvec{e}{\varvec{c}}_{\varvec{n}}\)

\(\:\varvec{R}\varvec{e}\varvec{c}{\varvec{a}}_{\varvec{l}}\)

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

\(\:\varvec{M}\varvec{C}\varvec{C}\)

TRPH (70%)

Meningioma

98.20

95.96

96.15

96.06

94.89

Glioma

97.60

97.64

97.26

97.45

95.18

Pituitary

97.46

95.55

95.99

95.77

93.96

Average

97.75

96.38

96.47

96.43

94.68

TSPH (30%)

Meningioma

98.38

96.30

96.74

96.52

95.47

Glioma

97.31

96.51

97.65

97.08

94.58

Pituitary

97.20

96.45

94.44

95.44

93.43

Average

97.63

96.42

96.28

96.34

94.49