An Iterated Local Search Algorithm for Cell Nuclei Detection from Pap Smear Images

Diniz, Débora Nasser; Souza, Marcone Jamilson Freitas; Carneiro, Cláudia Martins; Ushizima, Daniela; Medeiros, Fátima N. S. de; Oliveira, Paulo H. C.; Bianchi, Andrea Gomes Campos

doi:10.5220/0007718303190327

Cited by 2 publications

(1 citation statement)

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“…Diniz et al [30] proposed a methodology using Simple Linear Iterative Clustering (SLIC), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Iterated Local Search (ILS) algorithms to segment nuclei in synthetic images based on their morphologic features. Using the irace package, López-Ibáñez et al [31] and Diniz et al [16] concluded that the important features for the methodology were minimum circularity, maximum intensity, and minimum area.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification

et al. 2021

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Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods.

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Section: Introductionmentioning

confidence: 99%