HMC-net: a ResNet fused hierarchical multi-scale cross-attention architecture for mammographic breast malignancy recognition incorporating explainable AI

test, HMC-Attention clearly outperformed standard ResNet50; learning curves for stable convergence with limited overfitting provided evidence-based support that mammogram analysis is both accurate and transparent: the new baseline for automated diagnostics. This framework unites sturdy, profound implementation of deep learning with medical elucidation, setting paths toward trustworthy computer-supported diagnostic tools.

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Publication Details

Journal
Frontiers in Oncology
Published
2026-04-13
DOI
https://doi.org/10.3389/fonc.2026.1787210
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.00
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article

HMC-net: a ResNet fused hierarchical multi-scale cross-attention architecture for mammographic breast malignancy recognition incorporating explainable AI

Razia Zia, Rahmat Ullah, Irfan Ahmed Usmani, Maria Fatima et al.
Frontiers in Oncology
AI in cancer detection
article

HMC-net: a ResNet fused hierarchical multi-scale cross-attention architecture for mammographic breast malignancy recognition incorporating explainable AI

Razia Zia, Rahmat Ullah, Irfan Ahmed Usmani, Maria Fatima, Dinara Turzhanova
article en

Abstract

test, HMC-Attention clearly outperformed standard ResNet50; learning curves for stable convergence with limited overfitting provided evidence-based support that mammogram analysis is both accurate and transparent: the new baseline for automated diagnostics. This framework unites sturdy, profound implementation of deep learning with medical elucidation, setting paths toward trustworthy computer-supported diagnostic tools.

Frontiers in OncologyVol. 16
University of Essex (GB), Iqra University (PK), Sir Syed University of Engineering and Technology (PK), Emam Reza Hospital (IR), Habib University (PK)
Good health and well-being
Openalex Percentile: Top 18%
AI in cancer detection
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