# A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification
## 4 Conclusions
; Funding acquisition, A.G.C.B., C.M.C., D.M.U. and M.J.F.S. ; Investigation, D.N.D., M.T.R., A.G.C.B., C.M.C., D.M.U., F.N.S.d.M. and M.J.F.S. ; Methodology, D.N.D., M.T.R., A.G.C.B., C.M.C. and M.J.F.S. ; Project administration, A.G.C.B., C.M.C. and M.J.F.S. ; Resources, A.G.C.B., C.M.C. and M.J.F.S. ; Software, D.N.D. ; Supervision, A.G.C.B., C.M.C. and M.J.F.S. ; Validation, D.N.D., M.T.R., A.G.C.B., C.M.C. and M.J.F.S. ; Visualization, D.N.D. and M.T.R. ; Writing--original draft, D.N.D. and M.T.R. ; Writing--review and editing, D.N.D., M.T.R., A.G.C.B., C.M.C., D.M.U., F.N.S.d.M. and M.J.F.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was funded by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brazil (CAPES)--Finance Code 001 (88882.459660/2019-01), Fundacao de Amparo a Pesquisa de Estado de Minas Gerais (FAPEMIG, grants PPM/CEX/FAPEMIG/676-17-1, and PPSUSAFAPEMIG/APO-03740-17), Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq, grants 303266/2019-8, 06/2015-306396/2015-7, 06/2019-305895/2019-2, 304673/2011-0, 308947/2020-7, 401120/2013/9), Pro-Reitoria de Pesquisa e Inovacao: PROPI/UFOP-19/2020, 23109.000928/2020-33, 23109.000929/2020-88, Moore-Sloan Foundation, and Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-05 CH11231. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The datasets used in this work are available from the CRIC Searchable Image Database ([[https://database.cric.com.br](https://database.cric.com.br)]([https://database.cric.com.br](https://database.cric.com.br)) (accessed on 24 March 2021)) and the Herlev Database ([[https://mde-lab.aegean.gr/index.php/downloads](https://mde-lab.aegean.gr/index.php/downloads)]([https://mde-lab.aegean.gr/index.php/downloads](https://mde-lab.aegean.gr/index.php/downloads)) (accessed on 24 January 2021)). **Acknowledgments:** The authors would like to thank the Universidade Federal de Ouro Preto (UFOP), the Center for Recognition and Inspection of Cells (CRIC), the Group of Optimization and Algorithms (GOAL), and the eXtented Reality 4 Good (XR4 GoodLab) for supporting this research. **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**  
The following abbreviations are used in this manuscript: \begin{tabular}{l l} ASC-H & Atypical Squamous Cells cannot exclude HSIL \\ ASC-US & Atypical Squamous Cells of Undetermined Significance \\ CLAHE & Contrast Limited Adaptive Histogram Equalization \\ CRIC & Center for Recognition and Inspection of Cells \\ DT & Decision Tree \\ EM & Expectation-maximization \\ FN & False Negative \\ FP & False Positive \\ GLCM & Gray Level Co-occurrence Matrix \\ GMM & Gaussian Mixture Model \\ HSIL & High-grade Squamous Intraepithelial Lesion \\ ILS & Iterated Local Search \\ k-NN & k-Nearest Neighbors \\ LBP & Local Binary Patterns \\ LSIL & Low-grade Squamous Intraepithelial Lesion \\ LSSVM & Least Square Support Vector Machine \\ MLP & Multilayer Perceptron \\ NILM & Negative for Intraepithelial Lesion or Malignancy \\ RF & Random Forest \\ SC & Squamous Carcinoma \\ SLIC & Simple Linear Iterative Clustering \\ SMOTE & Synthetic Minority Oversampling TEchnique \\ SVM & Support Vector Machine \\ TAS & Threshold Adjacency Statistics \\ \end{tabular}   
TN True Negative TP True Positive