Table 1.
Pixel quantity information for the four datasets.
Fig 1.
(a) Medical Dataset’s Spectral Information (b) Removal of noisy spectral bands.
Fig 2.
DGWCR flowchart.
Table 2.
The names and formulas of 38 spectral indices.
Fig 3.
Weight map of 38 spectral indices obtained from the relief algorithm.
Fig 4.
Main flowchart of the hyperspectral tumor image classification process.
Fig 5.
Prediction effect diagrams of SVM with 8-01 dataset based on different feature inputs.
(a) False color image. (b) Ground truth image. (c) OWS. (d) SIS. (e) TFS. (f) OWS + SIS. (g) OWS + TFS. (h) SIS + TFS. (i) All features combined.
Table 3.
Detection accuracies of OA, AA, and KA for SVM with different feature inputs on four datasets.
Fig 6.
Color images of living human brain tissue from three patients, their corresponding grayscale images, and SVM detection effect diagrams with three-feature fusion.
(a, b, c) Images from patient 12-01. (d, e, f) Images from patient 12-02. (g, h, i) Images from patient 20-01.
Table 4.
Detection accuracies of OA, AA, and KA for RF with different feature inputs on four datasets.
Fig 7.
Prediction effect diagrams of RF with 20-01 dataset based on different feature inputs.
(a) False color image, (b) Ground truth image, (c) OWS, (d) SIS, (e) TFS, (f) OWS + SIS, (g) OWS + TFS, (h) SIS + TFS, (i) All features combined.
Fig 8.
Color images of living human brain tissue from three patients, their corresponding grayscale images, and RF detection effect diagrams with three-feature fusion.
(a, b, c) Images from the 8-01 dataset. (d, e, f) Images from the 12-01 dataset. (g, h, i) Images from the 12-02 dataset.
Fig 9.
SVM and RF detection accuracy on different datasets: (a) 8-01, (b) 12-01, (c) 12-02, (d) 20-01.