Abstract
In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification. Classifying cells in Pap smear images is very challenging, and it is still of paramount importance for cytopathologists. The Pap test is a cervical cancer prevention test that tracks preneoplastic changes in cervical epithelial cells. Carrying out this exam is important in that early detection. It is directly related to a greater chance of curing or reducing the number of deaths caused by the disease. The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed. This work considers 10 deep convolutional neural networks and proposes an ensemble of the three best architectures to classify cervical cancer upon cell nuclei and reduce the professionals’ workload. The dataset used in the experiments is available in the Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database. Considering the metrics of precision, recall, F1-score, accuracy, and sensitivity, the proposed ensemble improves previous methods shown in the literature for two- and three-class classification. We also introduce the six-class classification outcome.
Highlights
Pap testing can detect cervical cancer upon tracks pre-neoplastic changes in cervical epithelial cells
Our methodology is based on the analysis of convolutional neural networks to perform the classification of cell nuclei obtained in images of Pap smears
This study investigated the performance of several convolutional neural network architectures for the classification of cervical cell nuclei obtained in Pap smears
Summary
Pap testing can detect cervical cancer upon tracks pre-neoplastic changes in cervical epithelial cells. The traditional method of Pap test is the conventional cytology. In the process of Pap smear collection, the professional responsible for the collection exposes the cervix with the introduction of a speculum. Using a spatula, the professional performs the collection of the cervical cells. There are approximately 15,000 fields per image (40× objective) on one slide with a collection of cellular samples from a conventional examination that must be manually analyzed under an optical microscope by a qualified cytopathologist. The workload can reach 100 smears per day. There is another recommendation that at least two professionals analyze the same smear to avoid false negatives. The procedure requires much technical knowledge on the specialist’s part, which reduces the number of people who can perform it and increases the examination cost due to the necessary specialized labor costs
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