Citation

Ur Rehman, Zaka and Ahmad Fauzi, Mohammad Faizal and Mohd Isa, Wan Noorshahida and Touhami, Meriem and Wadood, Arbab Sufyan and Jabbar, Muhammad Kashif (2025) Adaptive Feature Aggregation Enhanced by Using DenseNet for Robust Breast Cancer Histopathology Image Classification. In: 2025 IEEE Region 10 Conference, TENCON 2025, 27 October 2025 - 30 October 2025, Kota Kinabalu, Malaysia.

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Abstract

Accurate classification of breast cancer from histopathology images is critical for early diagnosis and effective treatment planning. While deep learning techniques—particularly convolutional neural networks (CNNs)—have achieved substantial success in medical image analysis, existing models often struggle with issues such as overfitting, channel redundancy, and insufficient focus on clinically salient features. To address these limitations, this paper introduces a deep learning framework that integrates an Adaptive Feature Aggregation (AFA) block into the DenseNet121 architecture. The proposed AFA module learns to emphasize important feature channels by modeling inter-channel dependencies through a lightweight attention mechanism, thereby improving the network’s ability to distinguish between benign and malignant tissue patterns. Extensive experiments were conducted on the BreakHis 400x histopathology dataset. The proposed model achieved 98% accuracy, 98% F1-score, and an AUC of 0.99, outperforming the baseline DenseNet model. Evaluation metrics such as precision, recall, ROC-AUC, and confusion matrix analysis confirm the robustness and effectiveness of the proposed method for breast cancer classification.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Breast cancer, deep learning, image classification
Subjects:	R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	20 Apr 2026 02:24
Last Modified:	20 Apr 2026 02:24
URII:	http://shdl.mmu.edu.my/id/eprint/15754

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