An ensemble method for nuclei detection of overlapping cervical cells.
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2021
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The 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).
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Image processing algorithm, Detection approach, Cervical cancer, Pap smear test
Citação
DINIZ, 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.