Classification of H&E Images via CNN Models with XAI Approaches, DeepDream Representations and Multiple Classifiers

Página do item simplificado
dc.contributor.authorNeves, Leandro Alves [UNESP]
dc.contributor.authorMartinez, João Manuel Cardoso [UNESP]
dc.contributor.authorda Costa Longo, Leonardo H. [UNESP]
dc.contributor.authorRoberto, Guilherme Freire
dc.contributor.authorTosta, Thaína Aparecida Azevedo
dc.contributor.authorde Faria, Paulo Rogério
dc.contributor.authorLoyola, Adriano Mota
dc.contributor.authorCardoso, Sérgio Vitorino
dc.contributor.authorSilva, Adriano Barbosa
dc.contributor.authordo Nascimento, Marcelo Zanchetta
dc.contributor.authorRozendo, Guilherme Botazzo [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Federal de Uberlândia (UFU)
dc.date.accessioned2023-07-29T13:57:17Z
dc.date.available2023-07-29T13:57:17Z
dc.date.issued2023-01-01
dc.description.abstractThe study of diseases via histological images with machine learning techniques has provided important advances for diagnostic support systems. In this project, a study was developed to classify patterns in histological images, based on the association of convolutional neural networks, explainable artificial intelligence techniques, DeepDream representations and multiple classifiers. The images under investigation were representatives of breast cancer, colorectal cancer, liver tissue, and oral dysplasia. The most relevant features were associated by applying the Relief algorithm. The classifiers used were Rotation Forest, Multilayer Perceptron, Logistic, Random Forest, Decorate, IBk, K*, and SVM. The main results were areas under the ROC curve ranging from 0.994 to 1, achieved with a maximum of 100 features. The collected information allows for expanding the use of consolidated techniques in the area of classification and pattern recognition, in addition to supporting future applications in computer-aided diagnosis.en
dc.description.affiliationDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, SP
dc.description.affiliationInstitute of Mathematics and Computer Science (ICMC) University of São Paulo (USP), Av. Trabalhador São-carlense, 400, SP
dc.description.affiliationScience and Technology Institute Federal University of São Paulo (UNIFESP), Avenida Cesare Mansueto Giulio Lattes, 1201, São Paulo
dc.description.affiliationDepartment of Histology and Morphology Institute of Biomedical Science Federal University of Uberlândia (UFU), Av. Amazonas, S/N, MG
dc.description.affiliationArea of Oral Pathology School of Dentistry Federal University of Uberlândia (UFU), R. Ceará - Umuarama, MG
dc.description.affiliationFaculty of Computer Science (FACOM) Federal University of Uberlândia (UFU), Avenida João Naves de Ávila 2121, Bl.B, MG
dc.description.affiliationUnespDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, SP
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)
dc.description.sponsorshipIdCNPq: #153904/2021-6
dc.description.sponsorshipIdFAPESP: #2022/03020-1
dc.description.sponsorshipIdCNPq: #311404/2021-9
dc.description.sponsorshipIdCNPq: #313643/2021-0
dc.description.sponsorshipIdFAPEMIG: #APQ-00578-18
dc.format.extent354-364
dc.identifierhttp://dx.doi.org/10.5220/0011839400003467
dc.identifier.citationInternational Conference on Enterprise Information Systems, ICEIS - Proceedings, v. 1, p. 354-364.
dc.identifier.doi10.5220/0011839400003467
dc.identifier.issn2184-4992
dc.identifier.scopus2-s2.0-85160750141
dc.identifier.urihttp://hdl.handle.net/11449/248918
dc.language.isoeng
dc.relation.ispartofInternational Conference on Enterprise Information Systems, ICEIS - Proceedings
dc.sourceScopus
dc.subjectClassification
dc.subjectDeepDream Representations
dc.subjectGrad-CAM
dc.subjectHistological Images
dc.subjectLIME
dc.titleClassification of H&E Images via CNN Models with XAI Approaches, DeepDream Representations and Multiple Classifiersen
dc.typeTrabalho apresentado em evento
unesp.author.orcid0000-0001-8580-7054[1]
unesp.author.orcid0000-0001-5883-2983[4]
unesp.author.orcid0000-0002-9291-8892[5]
unesp.author.orcid0000-0003-2650-3960[6]
unesp.author.orcid0000-0001-9707-9365[7]
unesp.author.orcid0000-0003-1809-0617[8]
unesp.author.orcid0000-0001-8999-1135[9]
unesp.author.orcid0000-0003-3537-0178[10]
unesp.author.orcid0000-0002-4123-8264[11]

Arquivos

Coleções

Artigos - Ciência da Computação e Estatística - IBILCE