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This work focuses on two of the steps present in such systems, more precisely, the identification of cervical lesions and their respective classification. The development of automatic methods for these tasks is associated with some shortcomings, such as acquiring sufficient and representative clinical data. These limitations are addressed through a hybrid pipeline based on a deep learning model (RetinaNet) for the detection of abnormal regions, combined with random forest and SVM classifiers for their categorization, and complemented by the use of domain knowledge in its design. Additionally, the nuclei in each detected region are segmented, providing a set of nuclei-specific features whose impact on the classification result is also studied. Each module is individually assessed in addition to the complete system, with the latter achieving a precision, recall and F1 score of 0.04, 0.20 and 0.07, respectively. Despite the low precision, the system demonstrates potential as an analysis support tool with the capability of increasing the overall sensitivity of the human examination process.<\/jats:p>","DOI":"10.1007\/978-3-030-90436-4_24","type":"book-chapter","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T16:05:40Z","timestamp":1638461140000},"page":"299-312","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Cervical Cancer Detection and\u00a0Classification in\u00a0Cytology Images Using a\u00a0Hybrid Approach"],"prefix":"10.1007","author":[{"given":"Eduardo L.","family":"Silva","sequence":"first","affiliation":[]},{"given":"Ana Filipa","family":"Sampaio","sequence":"additional","affiliation":[]},{"given":"Lu\u00eds F.","family":"Teixeira","sequence":"additional","affiliation":[]},{"given":"Maria Jo\u00e3o M.","family":"Vasconcelos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"24_CR1","unstructured":"World Health Organization. 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