HAFMAB-Net: hierarchical adaptive fusion based on multilevel attention-enhanced bottleneck neural network for breast histopathological cancer classification
[ X ]
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
SPRINGER LONDON
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Histological images play a crucial role in diagnosing diseases, especially breast cancer, which remains a major health concern for women worldwide. Computer-aided diagnosis tools significantly assist physicians in early detection and treatment planning, helping reduce mortality rates. Convolutional neural networks (CNNs) based on deep learning have proven effective in distinguishing benign from malignant breast cancers. In this context, HAFMAB-Net: Hierarchical Adaptive Fusion based on Multilevel Attention-Enhanced Bottleneck Neural Network, is proposed. The network comprises two pathways utilizing an enhanced Bottleneck architecture with attention mechanisms to extract both global and spatial features. It incorporates a Deeper Spatial Attention Aggregator Module to boost the representation of locative features by focusing on key spatial regions, improving the discriminative power of aggregated features. Additionally, a modified Adaptive Fusion Module combines the enhanced global and boosted spatial features into a comprehensive and enriched feature representation, which is subsequently used for classification. The proposed HAFMAB-Net was evaluated on the BACH dataset and further tested on the BreaKHis and LC25000 datasets to validate its robustness. The model achieved 99% accuracy on the BACH dataset, 98.99% accuracy on BreaKHis, 100% accuracy on each Colon, Lung, LC25000 datasets, respectively. These results highlight the HAFMAB-Net's efficiency, accuracy, and effectiveness in both multi-class and binary classification tasks, demonstrating its potential for broader applications in medical image analysis.
Açıklama
Anahtar Kelimeler
Attention mechanism, Bottleneck neural network, Breast histopathological image, Classification, Deep learning
Kaynak
Signal Image and Video Processing
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
19
Sayı
5
Künye
Abdulwahhab, A. H., Bayat, O., & Ibrahim, A. A. (2025). HAFMAB-Net: hierarchical adaptive fusion based on multilevel attention-enhanced bottleneck neural network for breast histopathological cancer classification. Signal, Image and Video Processing, 19(5), 410.
