Figure 1:
![Architecture of an LTrP via a Fourier descriptor [6].](https://sciendo-parsed.s3.eu-central-1.amazonaws.com/678caf4e082aa65dea3d247b/j_ijssis-2025-0054_fig_001.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=ASIA6AP2G7AKNURFCXED%2F20251219%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Date=20251219T134202Z&X-Amz-Expires=3600&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEOP%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaDGV1LWNlbnRyYWwtMSJIMEYCIQDtGSr1cGsM848LUDG2e%2FmyNTUxllSV9BgF74mx%2FEHtIQIhAIqPpIy%2FGlQM%2FUbyHllSu6iRNdFTaS3kZ1ucUt7oP1vhKsQFCKz%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEQAhoMOTYzMTM0Mjg5OTQwIgxj548Ukixq%2Fu%2FlqAoqmAX0gYteCZljM1vWEg0I7I2%2FKn0vg3aAnyNcgLGzLiNxozvAlKFwN0NSnu9s3FjjNff0dd3la0bjpTX5KYKbrfHzUppnRZo2gPfcuvvtIFHW5ByKJLva8sTEJ1TjfVlAYcnOY6Q%2Bitei59tIeb2BOIvIUk7fSGmMulyh7E8HyR9MPLEqvCY4SQl7bVRUfEtvDMXRByyvLz8FeLwPFssQT999BcVN20U20s1702v9sLRepE95%2FQX8Zh79MD%2FBYn3RepvFTy%2BENEp84SyhvThtvStqfCbD67E%2BOrcHGmzs%2BKeUsElBPth0YdGnfuys7o5%2BAdIHpIcdNVivoyA9wjaW1MxC%2BveFIoi%2BKz9KlbOO%2BsFgh%2F5GSMMOdB%2BmOZxQWKoM3AyBzupno0jVYqbJQQzWKnSY2XXKoJfTszkH10YK1IrzjyDaWPeV1zC8CG5lmbuG6HILD5X2lYfUGp1tshI8muhlWluunZ%2BYbEh3KZfTN8T08epG2i8G%2Bu23o87vy2hvLCXri6vyuXG49KbcsLvOnkVitF4bamddx11kxMhmu5XKh0r15NyPB%2Fg3djqxtkPKL%2FaY3NsVDX8ebCbRbCWWm1HV1EE6E7dIvVh1ISQOIo97v6FcwlVz%2BZgsGDfNDnuHRyJkZQiIeaivZFvW9LOUTSEmoDIlbtt9PuVIJFCgMi3EaRoVm%2BNTR%2FjIGy3Cu3n5jHn6kM5Xsh3O1a4Ls3q6sRh43utdBsCMHM56BBkwt9p7vsSjLA45kntimi2ASK00I4RWtQRQND3S1Y%2Bwr0uaQURdRoQ2wbgAp3RIh%2FgAfNIBrmp96OlbFRGqhTchsQvcfZ1NxIg7lW4i20Vzhxufp3i6aFz6P97JTxVISd2821N1yDHhasO7lBsNMKLUlMoGOrABQHryt6EnViAYhKoqgk2ZaKydoebfLk%2FzVhnmP8tdm27yfJXpnTd2m5SD0vNaV931F3yyHMG2QpsvK2O8nr3Rh6gVnQgnJ7Wrlv6cwWBa5QkQllC%2BBqeOkqnSLiwLFRJKfQyPmi%2BVegyrQEYvY1dWCC04XM09%2FU%2BRG08lvZa9nz1iOZyC3%2F7rTWE3GxDtDUJ3kIsL7s9o5KC6XUS%2Bn7WKpv7V2Byjbdf0koR41lvaPUk%3D&X-Amz-Signature=6b88f1e60f8fc459c858fc52137aa74403c60da90657176c5426de0027c8e887&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject)
Figure 2:
![Approach used by Yang et al. [10].](https://sciendo-parsed.s3.eu-central-1.amazonaws.com/678caf4e082aa65dea3d247b/j_ijssis-2025-0054_fig_002.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=ASIA6AP2G7AKNURFCXED%2F20251219%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Date=20251219T134202Z&X-Amz-Expires=3600&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEOP%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaDGV1LWNlbnRyYWwtMSJIMEYCIQDtGSr1cGsM848LUDG2e%2FmyNTUxllSV9BgF74mx%2FEHtIQIhAIqPpIy%2FGlQM%2FUbyHllSu6iRNdFTaS3kZ1ucUt7oP1vhKsQFCKz%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEQAhoMOTYzMTM0Mjg5OTQwIgxj548Ukixq%2Fu%2FlqAoqmAX0gYteCZljM1vWEg0I7I2%2FKn0vg3aAnyNcgLGzLiNxozvAlKFwN0NSnu9s3FjjNff0dd3la0bjpTX5KYKbrfHzUppnRZo2gPfcuvvtIFHW5ByKJLva8sTEJ1TjfVlAYcnOY6Q%2Bitei59tIeb2BOIvIUk7fSGmMulyh7E8HyR9MPLEqvCY4SQl7bVRUfEtvDMXRByyvLz8FeLwPFssQT999BcVN20U20s1702v9sLRepE95%2FQX8Zh79MD%2FBYn3RepvFTy%2BENEp84SyhvThtvStqfCbD67E%2BOrcHGmzs%2BKeUsElBPth0YdGnfuys7o5%2BAdIHpIcdNVivoyA9wjaW1MxC%2BveFIoi%2BKz9KlbOO%2BsFgh%2F5GSMMOdB%2BmOZxQWKoM3AyBzupno0jVYqbJQQzWKnSY2XXKoJfTszkH10YK1IrzjyDaWPeV1zC8CG5lmbuG6HILD5X2lYfUGp1tshI8muhlWluunZ%2BYbEh3KZfTN8T08epG2i8G%2Bu23o87vy2hvLCXri6vyuXG49KbcsLvOnkVitF4bamddx11kxMhmu5XKh0r15NyPB%2Fg3djqxtkPKL%2FaY3NsVDX8ebCbRbCWWm1HV1EE6E7dIvVh1ISQOIo97v6FcwlVz%2BZgsGDfNDnuHRyJkZQiIeaivZFvW9LOUTSEmoDIlbtt9PuVIJFCgMi3EaRoVm%2BNTR%2FjIGy3Cu3n5jHn6kM5Xsh3O1a4Ls3q6sRh43utdBsCMHM56BBkwt9p7vsSjLA45kntimi2ASK00I4RWtQRQND3S1Y%2Bwr0uaQURdRoQ2wbgAp3RIh%2FgAfNIBrmp96OlbFRGqhTchsQvcfZ1NxIg7lW4i20Vzhxufp3i6aFz6P97JTxVISd2821N1yDHhasO7lBsNMKLUlMoGOrABQHryt6EnViAYhKoqgk2ZaKydoebfLk%2FzVhnmP8tdm27yfJXpnTd2m5SD0vNaV931F3yyHMG2QpsvK2O8nr3Rh6gVnQgnJ7Wrlv6cwWBa5QkQllC%2BBqeOkqnSLiwLFRJKfQyPmi%2BVegyrQEYvY1dWCC04XM09%2FU%2BRG08lvZa9nz1iOZyC3%2F7rTWE3GxDtDUJ3kIsL7s9o5KC6XUS%2Bn7WKpv7V2Byjbdf0koR41lvaPUk%3D&X-Amz-Signature=f93b2ef44a0a0004bb6651705d5fcbce7773c406fa66bbe7ec49d5f9416f9a7a&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject)
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Comparison of results based on different classifiers using fused features of image (brats2018)
| Number of features used | Technique | Feature extraction | Fusion method | Accuracy | Computational time (s) |
|---|---|---|---|---|---|
| 5 | SVM | LTcP | PLS | 81.5 | 82.3 |
| 5 | Naïve Bayes | 81.7 | 81.9 | ||
| 5 | Softmax | 82.2 | 83.8 | ||
| 5 | Decision tree | 81.8 | 82.6 | ||
| 10 | SVM | LTcP | PLS | 83.5 | 85.3 |
| 10 | Naïve Bayes | 84.3 | 82.1 | ||
| 10 | Softmax | 80.5 | 83.8 | ||
| 10 | Decision tree | 81.6 | 85.4 | ||
| 15 | SVM | LTcP | PLS | 84.7 | 88.9 |
| 15 | Naïve Bayes | 84.6 | 87.9 | ||
| 15 | Softmax | 82.3 | 85.4 | ||
| 15 | Decision tree | 86.5 | 86.6 | ||
| 20 | SVM | LTcP | PLS | 85.6 | 91.7 |
| 20 | Naïve Bayes | 84.6 | 92.6 | ||
| 20 | Softmax | 81.3 | 97.8 | ||
| 20 | Decision tree | 82.5 | 99.3 | ||
| 25 | SVM | LTcP | PLS | 85.2 | 92.4 |
| 25 | Naïve Bayes | 83.5 | 98.7 | ||
| 25 | Softmax | 81.2 | 101.4 | ||
| 25 | Decision tree | 83.7 | 109 | ||
| 30 | SVM | LTcP | PLS | 85.2 | 92.6 |
| 30 | Naïve Bayes | 85.6 | 99.4 | ||
| 30 | Softmax | 81.2 | 104.8 | ||
| 30 | Decision tree | 83.6 | 111.3 | ||
| 35 | SVM | LTcp | PLS | 85.8 | 94.4 |
| 35 | Naïve Bayes | 84.6 | 98.7 | ||
| 35 | Softmax | 85.8 | 105.3 | ||
| 35 | Decision tree | 84.6 | 118.2 |
Numbers of features extracted using the proposed method
| Method (features extraction) | Number of features |
|---|---|
| LTcP (proposed method) | 42 |
| LTrP [18] | 35 |
| LBP [15] | 10 |
| GLCM [26] | 19 |
| GLRM [27] | 7 |
Comparison of results based on different classifiers using various numbers of fused features of the image (brats2019)
| Number of features used | Technique | Feature extraction | Fusion method | Accuracy | Computational time (s) |
|---|---|---|---|---|---|
| −5 | SVM | LTcP | PLS | 80.2 | 82.3 |
| 5 | Naïve Bayes | 79.6 | 81.9 | ||
| 5 | Softmax | 79.2 | 83.8 | ||
| 5 | Decision tree | 75 | 82.6 | ||
| 10 | SVM | LTcP | PLS | 82.3 | 85.3 |
| 10 | Naïve Bayes | 80.1 | 82.1 | ||
| 10 | Softmax | 82.2 | 83.8 | ||
| 10 | Decision tree | 79.6 | 85.4 | ||
| 15 | SVM | LTcP | PLS | 87.8 | 88.9 |
| 15 | Naïve Bayes | 85.4 | 87.9 | ||
| 15 | Softmax | 87.3 | 85.4 | ||
| 15 | Decision tree | 84.8 | 86.6 | ||
| 20 | SVM | LTcP | PLS | 88.6 | 91.7 |
| 20 | Naïve Bayes | 88.5 | 92.6 | ||
| 20 | Softmax | 89.8 | 97.8 | ||
| 20 | Decision tree | 87.5 | 99.3 | ||
| 25 | SVM | LTcP | PLS | 89.7 | 92.4 |
| 25 | Naïve Bayes | 86.7 | 98.7 | ||
| 25 | Softmax | 88.4 | 101.4 | ||
| 25 | Decision tree | 87.5 | 109 | ||
| 30 | SVM | LTcP | PLS | 91.4 | 92.6 |
| 30 | Naïve Bayes | 90.2 | 99.7 | ||
| 30 | Softmax | 87.5 | 105.4 | ||
| 30 | Decision tree | 89.4 | 112.5 | ||
| 35 | SVM | LTcp | PLS | 91.4 | 94.4 |
| 35 | Naïve Bayes | 90.2 | 98.7 | ||
| 35 | Softmax | 90.3 | 106.1 | ||
| 35 | Decision tree | 90.5 | 120.3 |
Comparison among approaches based on recall and F1 score on the brats2018 dataset
| Approach | Recall | F1 score |
|---|---|---|
| SVM | 94.60 | 93.20 |
| Naïve Bayes | 89.45 | 88.80 |
| Softmax | 94.45 | 92.93 |
| Decision Tree | 89.34 | 89.35 |
| Proposed approach (SVM as classifier) | 95.90 | 96.80 |
| Proposed approach (Naïve Bayes as classifier) | 94.03 | 94.5 |
| Proposed approach (Softmax as classifier) | 95.10 | 96.95 |
| Proposed approach (decision tree as classifier) | 93.20 | 94.10 |