Selection of CNN, Haralick and Fractal Features Based on Evolutionary Algorithms for Classification of Histological Images

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.creatorCandelero, David [UNESP]-
Autor(es): dc.creatorRoberto, Guilherme Freire-
Autor(es): dc.creatorDo Nascimento, Marcelo Zanchetta-
Autor(es): dc.creatorRozendo, Guilherme Botazzo [UNESP]-
Autor(es): dc.creatorNeves, Leandro Alves [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:49:35Z-
Data de disponibilização: dc.date.available2022-02-22T00:49:35Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2020-12-15-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/BIBM49941.2020.9313328-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/207226-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/207226-
Descrição: dc.descriptionThe analysis of histological image features for automatic detection of pathologies plays an important role in medicine. Considering that, we proposed a method based on the association of features extracted by multi-scale and multidimensional fractal techniques, Haralick descriptors, and CNN for pattern recognition of colorectal cancer, breast cancer, and non-Hodgkin lymphomas. For feature selection, we applied the ReliefF algorithm to rank the best 50 features and then applied the evolutionary algorithms GWO, PSO, and GA. The classification was made with SVM, K*, and Random Forest algorithms. This strategy allows classifying plenty of feature vectors selected by different algorithms, and consequently, improves the accuracy of the interpretations about the class distinction of histological images. The best combination found was composed of GA and K* algorithms, resulting in 91.06%, 90.52% e 82.01% accuracy for colorectal cancer, breast cancer, and non-Hodgkin lymphomas respectively. The performance obtained by the method indicates that the feature association extracted by different approaches and their subsequent selection and classification presents a potential field for further studies with a high degree of contribution to science.-
Descrição: dc.descriptionSão Paulo State University (UNESP) Dep. of Computer Science and Statistics (DCCE)-
Descrição: dc.descriptionFederal University of Uberlândia (UFU) Faculty of Computer Science (FACOM)-
Descrição: dc.descriptionSão Paulo State University (UNESP) Dep. of Computer Science and Statistics (DCCE)-
Formato: dc.format2709-2716-
Idioma: dc.languageen-
Relação: dc.relationProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020-
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Palavras-chave: dc.subjectCNN-
Palavras-chave: dc.subjectfeature selection-
Palavras-chave: dc.subjectfractal geometry-
Palavras-chave: dc.subjectHaralick descriptors-
Palavras-chave: dc.subjecthistological images-
Título: dc.titleSelection of CNN, Haralick and Fractal Features Based on Evolutionary Algorithms for Classification of Histological Images-
Aparece nas coleções:Repositório Institucional - Unesp

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