Diniz, Débora NasserVitor, Rafael FerreiraBianchi, Andrea Gomes CamposSilva, Saul Emanuel DelabridaCarneiro, Cláudia MartinsUshizima, Daniela MayumiMedeiros, Fátima Nelsizeuma Sombra deSouza, Marcone Jamilson Freitas2022-12-062022-12-062021DINIZ, D. N. et al. An ensemble method for nuclei detection of overlapping cervical cells. Expert Systems With Applications, v. 185, artigo 115642, 2021. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0957417421010356>. Acesso em: 06 jul. 2022.0957-4174http://www.repositorio.ufop.br/jspui/handle/123456789/15839The Pap test is a preventive approach that requires specialized and labor-intensive examination of cytological preparations to track potentially cancerous cells from the internal and external cervix surface. A cytopathologist must analyze many microscopic fields while screening for abnormal cells. Therefore there is hope that a support decision system could assist with clinical diagnosis, for example, by identifying sub-cellular abnormalities, such as changes in the nuclei features. This work proposes an ensemble method for cervical nuclei detection aiming to reduce the workload of cytopathologists. First, a preprocessing phase divides the original image into superpixels, which are input to feature extraction and selection algorithms. The proposed ensemble method combines three classifiers: Decision Tree (DT), Nearest Centroid (NC), and k-Nearest Neighbors (k-NN), which are evaluated against the ISBI’14 Overlapping Cervical Cytology Image Segmentation Challenge dataset. Experiments show that the proposed method is the state-of-the-art algorithm of the literature for recall (0.999) and F1 values (0.993). It produced a recall very close to the optimum value and also kept high precision (0.988).en-USrestritoImage processing algorithmDetection approachCervical cancerPap smear testArtigo publicado em periodicohttps://www.sciencedirect.com/science/article/pii/S0957417421010356https://doi.org/10.1016/j.eswa.2021.115642