Evaluation of machine learning models for the classification of breast cancer hormone receptors using micro-FTIR images

VALLE, MATHEUS delSANTOS, MOISES O. dosSANTOS, SOFIA N. dosBERNARDES, EMERSON S.ZEZELL, DENISE M.2021-11-122021-11-12VALLE, MATHEUS del; SANTOS, MOISES O. dos; SANTOS, SOFIA N. dos; BERNARDES, EMERSON S.; ZEZELL, DENISE M. Evaluation of machine learning models for the classification of breast cancer hormone receptors using micro-FTIR images. In: SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE, May 31 - June 2, 2021, Online. <b>Proceedings...</b> Piscataway, NJ, USA: IEEE, 2021. DOI: <a href="https://dx.doi.org/10.1109/SBFOTONIOPC50774.2021.9461946">10.1109/SBFOTONIOPC50774.2021.9461946</a>. Disponível em: http://repositorio.ipen.br/handle/123456789/32334.http://repositorio.ipen.br/handle/123456789/32334The breast cancer is the most incident cancer in women. Evaluation of hormone receptors expression plays an important role to outline treatment strategies. FTIR spectroscopy imaging may be employed as an additional technique, providing extra information to help physicians. In this work, estrogen and progesterone receptors expression were evaluated using tumors biopsies from human cell lines inoculated in mice. FTIR images were collect from histological sections, and six machine learning models were applied and assessed. Xtreme gradient boost and Linear Discriminant Analysis presented the best accuracies results, indicating to be potential models for breast cancer classification tasks.openAccessmammary glandsneoplasmsfourier transformationimagesmachine learninghormonesreceptorsEvaluation of machine learning models for the classification of breast cancer hormone receptors using micro-FTIR imagesTexto completo de evento10.1109/SBFOTONIOPC50774.2021.94619460000-0001-7404-96060000-0002-0029-7313https://orcid.org/0000-0002-0029-7313https://orcid.org/0000-0001-7404-9606