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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Accesso online: | https://arxiv.org/abs/2503.08756 |
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| _version_ | 1866913730962391040 |
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| author | Arizmendi, Carlos Vellido, Alfredo Romero, Enrique |
| author_facet | Arizmendi, Carlos Vellido, Alfredo Romero, Enrique |
| contents | The diagnosis of brain tumours is an extremely sensitive and complex clinical task that must rely upon information gathered through non-invasive techniques. One such technique is magnetic resonance, in the modalities of imaging or spectroscopy. The latter provides plenty of metabolic information about the tumour tissue, but its high dimensionality makes resorting to pattern recognition techniques advisable. In this brief paper, an international database of brain tumours is analyzed resorting to an ad hoc spectral frequency selection procedure combined with nonlinear classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08756 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Frequency selection for the diagnostic characterization of human brain tumours Arizmendi, Carlos Vellido, Alfredo Romero, Enrique Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Applications Computation The diagnosis of brain tumours is an extremely sensitive and complex clinical task that must rely upon information gathered through non-invasive techniques. One such technique is magnetic resonance, in the modalities of imaging or spectroscopy. The latter provides plenty of metabolic information about the tumour tissue, but its high dimensionality makes resorting to pattern recognition techniques advisable. In this brief paper, an international database of brain tumours is analyzed resorting to an ad hoc spectral frequency selection procedure combined with nonlinear classification. |
| title | Frequency selection for the diagnostic characterization of human brain tumours |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Applications Computation |
| url | https://arxiv.org/abs/2503.08756 |