Publication: A Comparative Analysis of Machine Learning and Deep Learning Approaches for Multiclass Nucleus Classifcation in Histological Images
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Date
2026-01-30
Authors
Antonio Luis Sánchez-Torres ; Jesús García-Salmerón ; Pilar González-Férez ; Gregorio Bernabé ; José Manuel García
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Facultades de la UMU::Facultad de Informática
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Publisher
Wiley
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Kalapraveen Bagadi
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DOI
https://doi.org/10.1155/acis/4540418
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info:eu-repo/semantics/article
Description
Abstract
Precisely 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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Citation
Sánchez-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
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