| Texto completo | |
| Autor(es): |
Peres, Daniella Lumara
;
Germano, Gleice
;
Silva, Daniela F. T.
;
Bachmann, Luciano
;
Matos, Leandro L. de
;
Felipe, Joaquim C.
;
Pereira, Thiago Martini
;
Zezell, Denise Maria
Número total de Autores: 8
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2024 SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE, SBFOTON IOPC 2024; v. N/A, p. 3-pg., 2024-01-01. |
| Resumo | |
Early detection and diagnosis of oral squamous cell carcinoma (OSCC) are essential for improving patient outcomes. This study presents the development of a deep neural network model trained to classify OSCC using hyperspectral FTIR spectroscopy imaging. The network's performance was evaluated using accuracy, precision, recall, and F1-score metrics. The model demonstrated high effectiveness in classifying unfolded OSCC hyperspectral images, showing potential as a valuable tool for supporting diagnostic processes in clinical settings. (AU) | |
| Processo FAPESP: | 21/00633-0 - Processamento e análise de sinais hiperespectrais aplicados ao diagnóstico histopatológico |
| Beneficiário: | Luciano Bachmann |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |