Abstract
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.
Highlights
The World Health Organization recently estimated 605,000 new cases and 342,000 deaths from cervical cancer worldwide [1]
The balancing techniques applied to the Herlev database performed to the Center for Recognition and Inspection of Cells (CRIC) database, so these results were not presented
It is possible to observe that the Random Forest (RF) classifier performs better than k-Nearest Neighbors (k-NN), Ridge, and Decision Tree (DT), considering all metrics and number of classes
Summary
The World Health Organization recently estimated 605,000 new cases and 342,000 deaths from cervical cancer worldwide [1]. The use of the Pap smear test for population-based cervical cytological screening has shown remarkable success in the early detection of such cancers; despite this, there is much to improve within this program [2,3]. The recommendation worldwide of the daily hours worked varies depending on the country: in Canada, it is 80 smears/day; in Brazil, it is 70 smears/day, and in the United States, it is 100 smears/day [5,6]. This scenario encompasses tiring and repetitive work that leads to errors inherent in human visual interpretation. Investigations conducted since before the 1990s show rates of 2% to 62% false-negatives in Pap test results [7,8,9,10,11]
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