A fuzzy distance-based ensemble of deep models for cervical cancer detection

Página do item simplificado
dc.contributor.authorPramanik, Rishav
dc.contributor.authorBiswas, Momojit
dc.contributor.authorSen, Shibaprasad
dc.contributor.authorSouza Júnior, Luis Antonio de
dc.contributor.authorPapa, João Paulo
dc.contributor.authorSarkar, Ram
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionRegensburg Medical Image Computing (ReMIC)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-04-28T19:52:56Z
dc.date.available2022-04-28T19:52:56Z
dc.date.issued2022-06-01
dc.description.abstractBackground and Objective: Cervical cancer is one of the leading causes of women's death. Like any other disease, cervical cancer's early detection and treatment with the best possible medical advice are the paramount steps that should be taken to ensure the minimization of after-effects of contracting this disease. PaP smear images are one the most effective ways to detect the presence of such type of cancer. This article proposes a fuzzy distance-based ensemble approach composed of deep learning models for cervical cancer detection in PaP smear images. Methods: We employ three transfer learning models for this task: Inception V3, MobileNet V2, and Inception ResNet V2, with additional layers to learn data-specific features. To aggregate the outcomes of these models, we propose a novel ensemble method based on the minimization of error values between the observed and the ground-truth. For samples with multiple predictions, we first take three distance measures, i.e., Euclidean, Manhattan (City-Block), and Cosine, for each class from their corresponding best possible solution. We then defuzzify these distance measures using the product rule to calculate the final predictions. Results: In the current experiments, we have achieved 95.30%, 93.92%, and 96.44% respectively when Inception V3, MobileNet V2, and Inception ResNet V2 run individually. After applying the proposed ensemble technique, the performance reaches 96.96% which is higher than the individual models. Conclusion: Experimental outcomes on three publicly available datasets ensure that the proposed model presents competitive results compared to state-of-the-art methods. The proposed approach provides an end-to-end classification technique to detect cervical cancer from PaP smear images. This may help the medical professionals for better treatment of the cervical cancer. Thus increasing the overall efficiency in the whole testing process. The source code of the proposed work can be found in github.com/rishavpramanik/CervicalFuzzyDistanceEnsemble.en
dc.description.affiliationDepartment of Computer Science and Engineering, Jadavpur University, 188 Raja S C Mallick Rd, West Bengal
dc.description.affiliationDepartment of Metallurgical and Material Engineering, Jadavpur University, 188 Raja S C Mallick Rd, West Bengal
dc.description.affiliationDepartment of Computer Science and Technology, University of Engineering and Management, West Bengal
dc.description.affiliationDepartment of Computing, São Carlos Federal University-UFScar, São Paulo
dc.description.affiliationRegensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Bavaria
dc.description.affiliationDepartment of Computing, São Paulo State University, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01, São Paulo
dc.identifierhttp://dx.doi.org/10.1016/j.cmpb.2022.106776
dc.identifier.citationComputer Methods and Programs in Biomedicine, v. 219.
dc.identifier.doi10.1016/j.cmpb.2022.106776
dc.identifier.issn1872-7565
dc.identifier.issn0169-2607
dc.identifier.scopus2-s2.0-85127673130
dc.identifier.urihttp://hdl.handle.net/11449/223771
dc.language.isoeng
dc.relation.ispartofComputer Methods and Programs in Biomedicine
dc.sourceScopus
dc.subjectCervical cancer
dc.subjectComputer-aided detection
dc.subjectDeep learning
dc.subjectEnsemble learning
dc.subjectFuzzy logic
dc.titleA fuzzy distance-based ensemble of deep models for cervical cancer detectionen
dc.typeArtigo
unesp.author.orcid0000-0003-0144-8539[1]
unesp.author.orcid0000-0003-2820-0867[2]
unesp.author.orcid0000-0003-4815-6621[3]
unesp.author.orcid0000-0001-8813-4086[6]

Arquivos

Coleções

Artigos