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Paper details
Number 2 - June 2025
Volume 35 - 2025
Exploring data preparation strategies: A comparative analysis of vision transformer and ConvNeXT architectures in breast cancer histopathology classification
Mikołaj Kaczmarek, Marek Kowal, Józef Korbicz
Abstract
Breast cancer remains a major global health challenge and the accurate classification of histopathological samples into
benign and malignant categories is critical for effective diagnosis and treatment planning. This study offers a comparative
analysis of two state-of-the-art deep learning architectures, Vision Transformer (ViT) and ConvNeXT for breast cancer
histopathology image classification, focusing on the impact of data preparation strategies. Using the BreakHis benchmark
dataset, we investigated six distinct preprocessing approaches, including image resizing, patch-based techniques, and cellular
content filtering, applied across four magnification levels (40×, 100×, 200×, and 400×). Both models were fine-tuned
and evaluated using multiple performance metrics: accuracy, precision, recall, F1 score, and area under the receiver operating
characteristic curve (AUC). The results highlight the critical influence of data preparation on model performance. ViT
achieved its highest accuracy of 95.6% and an F1 score of 96.8% at 40× magnification with randomly generated patches.
ConvNeXT demonstrated strong robustness across scenarios, attaining a precision of 98.5% at 100× magnification using
non-overlapping patches. These findings emphasize the importance of customized data preprocessing and informed model
selection in improving diagnostic accuracy. Optimizing both architectural design and data handling is essential to enhancing
the reliability of automated histopathological analysis and supporting clinical decision-making.
Keywords
vision transformer, ConvNeXT, BreakHis, data preparation, image classification