AutoEffFusionNet: A New Approach for Cervical Cancer Diagnosis Using ResNet-based Autoencoder with Attention Mechanism and Genetic Feature Selection

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dc.contributor.authorYahya Dogan
dc.date.accessioned2025-03-11T12:57:15Z
dc.date.available2025-03-11T12:57:15Z
dc.date.issued2025
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractCervical cancer poses a significant global health challenge, necessitating accurate and efficient diagnostic solutions. This study introduces a novel hybrid framework, AutoEffFusionNet, that integrates unsupervised feature learning through ResNet-based autoencoders with attention mechanisms and supervised learning via transfer learning models. By leveraging the complementary strengths of these approaches, the proposed method achieves enhanced diagnostic accuracy in cervical cancer classification. Genetic algorithms optimize the feature selection process, retaining only the most relevant attributes, thereby addressing feature redundancy and improving computational efficiency. The selected features are then classified using a Support Vector Machine, effectively combining deep learning's feature extraction capabilities with machine learning's robust classification strengths. Additionally, Grad-CAM visualizations are incorporated to highlight critical regions influencing the classification decisions, enhancing interpretability and transparency. The framework was rigorously evaluated on two benchmark datasets, SIPaKMeD, and Mendeley LBC, achieving remarkable accuracies of 99.26% and 100%, respectively. These results demonstrate the effectiveness of the proposed model in addressing key challenges in cervical cancer diagnosis and its potential for deployment in clinical applications.
dc.identifier.citationDogan, Y. (2025). AutoEffFusionNet: A New Approach for Cervical Cancer Diagnosis Using ResNet-based Autoencoder with Attention Mechanism and Genetic Feature Selection. IEEE Access.
dc.identifier.doi10.1109/access.2025.3543850
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85218719730
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1109/access.2025.3543850
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8557
dc.indekslendigikaynakScopus
dc.institutionauthorDoÄŸan, Yahya
dc.institutionauthorid0000-0003-1529-6118
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofIEEE Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectautoencoder
dc.subjectCervical cancer diagnosis
dc.subjectCMAB
dc.subjectgenetic algorithm
dc.subjectsupport vector machine
dc.subjecttransfer learning
dc.titleAutoEffFusionNet: A New Approach for Cervical Cancer Diagnosis Using ResNet-based Autoencoder with Attention Mechanism and Genetic Feature Selection
dc.typejournal-article

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