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A Comparative Analysis of Machine Learning and Deep Learning Approaches for Multiclass Nucleus Classifcation in Histological Images

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dc.contributor.authorSánchez-Torres, Antonio Luis
dc.contributor.authorGarcía-Salmerón, Jesús
dc.contributor.authorGonzález Férez, María Pilar
dc.contributor.authorBernabé García, Gregorio
dc.contributor.authorGarcía Carrasco, José Manuel
dc.contributor.departmentIngeniería y Tecnología de Computadores
dc.contributor.editorKalapraveen Bagadi
dc.contributor.otherFacultades de la UMU::Facultad de Informática
dc.date.accessioned2026-01-30T17:58:11Z
dc.date.available2026-01-30T17:58:11Z
dc.date.copyright© 2026 Antonio Luis Sánchez-Torres et al. Applied Computational Intelligence and Soft Computing published by John Wiley & Sons Ltd.
dc.date.issued2026-01-30
dc.description.abstractPrecisely classifying cells in histological images is critical for early cancer diagnosis and tumor assessment. Traditional manual methods are time-consuming and labor-intensive for histopathologists, driving the development of automated approaches using machine learning (ML) and deep learning (DL). Convolutional neural networks (CNNs) and, more recently, vision transformers (ViTs) have demonstrated signifcant potential in addressing the challenges of cell classifcation by leveraging their ability to automatically extract and learn complex features from histological images. In this work, we evaluate multiple classifcation architectures applied to stained histological images to determine their efectiveness in identifying cancerous cells. We compare traditional ML models, which rely on manually extracted features such as shape and texture, against two DL-based classifers: a CNN-based model (ResNet50) and a ViT-based model. To optimize ML models, we apply principal component analysis (PCA) to refne feature selection. Meanwhile, DL models are trained on cropped cell images using two preprocessing strategies: one that includes additional surrounding cellular context and another that uses only the cell pixels. Additionally, we investigate class balancing strategies, including downsampling and oversampling through data augmentation, to mitigate the efects of dataset imbalance. Experimental results highlight the clear advantage of DL models over traditional ML approaches. ResNet50 consistently delivers robust and reliable performance across diferent preprocessing strategies, confrming its efectiveness for histopathological classifcation tasks. Meanwhile, ViTs achieve results that are comparable to those of CNNs while demonstrating a distinct advantage in classifying underrepresented nucleus classes, likely due to their ability to capture long-range dependencies. Furthermore, incorporating the surrounding cellular environment signifcantly improves classifcation accuracy, underscoring the importance of contextual information in distinguishing between diferent types of nuclei.
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dc.format.extent16
dc.identifier.citationSánchez-Torres, Antonio Luis, García-Salmerón, Jesús, González-Férez, Pilar, Bernabé, Gregorio, García, José Manuel, A Comparative Analysis of Machine Learning and Deep Learning Approaches for Multiclass Nucleus Classification in Histological Images, Applied Computational Intelligence and Soft Computing, 2026, 4540418, 16 pages, 2026. https://doi.org/10.1155/acis/4540418
dc.identifier.doihttps://doi.org/10.1155/acis/4540418
dc.identifier.eissn1687-9732
dc.identifier.issn1687-9724
dc.identifier.urihttp://hdl.handle.net/10201/197929
dc.languageeng
dc.publisherWiley
dc.relationGrant TED2021-129221B-I00 was funded by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR.” Proyecto de Investigación TED2021-129221B-I00
dc.relation.publisherversionhttps://onlinelibrary.wiley.com/doi/10.1155/acis/4540418
dc.rightsAttribution 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectHistological images
dc.subjectResidual neural networks
dc.subjectVision transformers
dc.subjectCell clasification
dc.subject.odsObjetivo 9: Infraestructura
dc.subject.odsObjetivo 3: Salud
dc.titleA Comparative Analysis of Machine Learning and Deep Learning Approaches for Multiclass Nucleus Classifcation in Histological Images
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
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