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- W4385755111 abstract "Polarimetric data is nowadays used in the biomedical field to inspect organic tissues or for the early detection of some pathologies. In this work, we present a thorough comparison between different classification models based on several sets of polarimetric data, this allowing us to choose the polarimetric framework to construct tissue classification models. Four different well-known machine learning models are compared by analyzing three polarimetric datasets: (i) a selection of ten representative polarimetric observables; (ii) the Mueller matrix elements; and (iii) the combination of (i) and (ii) datasets. The study is conducted on the experimental Mueller matrices images measured on different organic tissues: muscle, tendon, myotendinous junction and bone; all of them measured from a collection of 165 ex-vivo chicken thighs. Provided results show the potential of polarimetric datasets for classification of biological tissues and paves the way for future applications in biomedicine and clinical trials." @default.
- W4385755111 created "2023-08-12" @default.
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- W4385755111 date "2023-08-11" @default.
- W4385755111 modified "2023-10-15" @default.
- W4385755111 title "Optimizing the classification of biological tissues using polarized data supported by machine learning" @default.
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- W4385755111 doi "https://doi.org/10.1117/12.2673758" @default.
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