HAFMAB-Net: hierarchical adaptive fusion based on multilevel attention-enhanced bottleneck neural network for breast histopathological cancer classification

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dc.contributor.authorAbdulwahhab, Ali H.
dc.contributor.authorBayat, Oğuz
dc.contributor.authorIbrahim, Abdullahi Abdu
dc.date.accessioned2025-06-11T13:15:11Z
dc.date.available2025-06-11T13:15:11Z
dc.date.issued2025
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
dc.description.abstractHistological 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.
dc.identifier.citationAbdulwahhab, 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.
dc.identifier.doi10.1007/s11760-025-04001-1
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue5
dc.identifier.scopus2-s2.0-105000228482
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5766
dc.identifier.volume19
dc.identifier.wosWOS:001449039600001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.institutionauthorAbdulwahhab, Ali H.
dc.institutionauthorIbrahim, Abdullahi Abdu
dc.language.isoen
dc.publisherSPRINGER LONDON
dc.relation.ispartofSignal Image and Video Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Öğrenci
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAttention mechanism
dc.subjectBottleneck neural network
dc.subjectBreast histopathological image
dc.subjectClassification
dc.subjectDeep learning
dc.titleHAFMAB-Net: hierarchical adaptive fusion based on multilevel attention-enhanced bottleneck neural network for breast histopathological cancer classification
dc.typeArticle

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