Authors:
Daniela Terra
1
;
2
;
Adriano Lisboa
3
;
Mariana T. Rezende
4
;
Claudia Carneiro
4
and
Andrea Bianchi
2
Affiliations:
1
Department of Computing, Federal Institute of Minas Gerais, Ouro Branco, MG, Brazil
;
2
Department of Computing, Federal University of Ouro Preto, Ouro Preto, MG, Brazil
;
3
Research Department, GAIA, Belo Horizonte, MG, Brazil
;
4
Clinical Analysis Department, Federal University of Ouro Preto, Ouro Preto, MG, Brazil
Keyword(s):
Cervical Cancer, Image Classification, Morphological Features, Features Selection, XGBoost Classifier.
Abstract:
The diagnosis of cervical lesions is an interpretative process carried out by specialists based on cellular information from the nucleus and cytoplasm. Some authors have used cell nucleus detection and segmentation algorithms to support the computer-assisted diagnosis process. These approaches are based on the assumption that the nucleus contains the most important information for lesion detection. This work investigates the influence of morphological information from the nucleus, cytoplasm, and both on cervical cell diagnosis. Experiments were performed to analyze 3,233 real cells extracting from each one 200 attributes related to size, shape, and edge contours. Results showed that morphological attributes could accurately represent lesions in binary and ternary classifications. However, identifying specific cell anomalies like Bethesda System classes requires adding new attributes such as texture.