{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T20:54:38Z","timestamp":1769806478356,"version":"3.49.0"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"vor","delay-in-days":29,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>Precisely classifying cells in histological images is critical for early cancer diagnosis and tumor assessment. Traditional manual methods are time\u2010consuming and labor\u2010intensive 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 significant potential in addressing the challenges of cell classification by leveraging their ability to automatically extract and learn complex features from histological images. In this work, we evaluate multiple classification architectures applied to stained histological images to determine their effectiveness in identifying cancerous cells. We compare traditional ML models, which rely on manually extracted features such as shape and texture, against two DL\u2010based classifiers: a CNN\u2010based model (ResNet50) and a ViT\u2010based model. To optimize ML models, we apply principal component analysis (PCA) to refine 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 effects of dataset imbalance. Experimental results highlight the clear advantage of DL models over traditional ML approaches. ResNet50 consistently delivers robust and reliable performance across different preprocessing strategies, confirming its effectiveness for histopathological classification 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\u2010range dependencies. Furthermore, incorporating the surrounding cellular environment significantly improves classification accuracy, underscoring the importance of contextual information in distinguishing between different types of nuclei.<\/jats:p>","DOI":"10.1155\/acis\/4540418","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T09:57:21Z","timestamp":1769767041000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Comparative Analysis of Machine Learning and Deep Learning Approaches for Multiclass Nucleus Classification in Histological Images"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4136-6354","authenticated-orcid":false,"given":"Antonio Luis","family":"S\u00e1nchez-Torres","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4226-2198","authenticated-orcid":false,"given":"Jes\u00fas","family":"Garc\u00eda-Salmer\u00f3n","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1681-5442","authenticated-orcid":false,"given":"Pilar","family":"Gonz\u00e1lez-F\u00e9rez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7265-3508","authenticated-orcid":false,"given":"Gregorio","family":"Bernab\u00e9","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6388-2835","authenticated-orcid":false,"given":"Jos\u00e9 Manuel","family":"Garc\u00eda","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.l408"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2206.01728"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-cancerbio-062822-010523"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpi.2023.100357"},{"key":"e_1_2_10_6_2","unstructured":"DosovitskiyA. BeyerL. KolesnikovA.et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale 2021 https:\/\/arxiv.org\/abs\/2010.11929."},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105939"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/app13095521"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15123075"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.1.3.034003"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giac037"},{"key":"e_1_2_10_12_2","unstructured":"Tissue Image Analytics Centre PanNuke Dataset for Nuclei Instance Segmentation and Classification 2025 https:\/\/warwick.ac.uk\/fac\/cross_fac\/tia\/data\/pannuke Accessed: 2025-05-14."},{"key":"e_1_2_10_13_2","volume-title":"PanNuke: An Open Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification","author":"Gamper J.","year":"2019"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2212.13401"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-021-02403-0"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.1002\/gcc.23177"},{"key":"e_1_2_10_17_2","doi-asserted-by":"crossref","DOI":"10.1109\/ICSC64641.2025.00019","volume-title":"Evaluation of Hand-Crafted Features with Mask Images Obtained from PanNuke Dataset Using Bayesian Optimization and Machine Learning Models","author":"Yellu S.","year":"2025"},{"key":"e_1_2_10_18_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2003.10778"},{"key":"e_1_2_10_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101563"},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-025-01550-2"},{"key":"e_1_2_10_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103143"},{"key":"e_1_2_10_22_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2502.06307"},{"key":"e_1_2_10_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2023.3281864"},{"key":"e_1_2_10_24_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/7921922"},{"key":"e_1_2_10_25_2","doi-asserted-by":"publisher","DOI":"10.3390\/s25041207"},{"key":"e_1_2_10_26_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.4.2.021105"},{"key":"e_1_2_10_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1973.4309314"},{"key":"e_1_2_10_28_2","article-title":"The OpenCV Library","author":"Bradski G.","year":"2000","journal-title":"Dr. Dobb\u2019s Journal of Software Tools"},{"key":"e_1_2_10_29_2","doi-asserted-by":"publisher","DOI":"10.1162\/153244303322753616"},{"key":"e_1_2_10_30_2","unstructured":"HeK. 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