Exploring Data Preparation Strategies: A Comparative Analysis of Vision Transformer and ConvNext Architectures in Breast Cancer Histopathology Classification

• Published in: International journal of applied mathematics and computer science Vol. 35; no. 2; pp. 329 - 339
• Main Authors: Kaczmarek, Mikołaj, Kowal, Marek, Korbicz, Józef
• Format: Journal Article
• Language: English
• Published: Zielona Góra Sciendo 01.06.2025; De Gruyter Poland
• Subjects: Accuracy; BreakHis; Breast cancer; Comparative analysis; ConvNeXT; data preparation; Design optimization; Histopathology; Image classification; Medical imaging; Performance evaluation; Performance measurement; Preprocessing; Public health; vision transformer
• ISSN: 1641-876X; 2083-8492
• DOI: 10.61822/amcs-2025-0023

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.