Improving Breast Cancer Diagnosis using Attention-Enhanced Hybrid CNN–Transformer Model

Aslı Nur Polat; Hussein Mahmood Abdo Mohammed

doi:10.17798/bitlisfen.1772185

Improving Breast Cancer Diagnosis using Attention-Enhanced Hybrid CNN–Transformer Model

Abstract

Breast cancer is the most common cancer among women and the most frequently diagnosed cancer worldwide. Recent advancements in deep learning have led to significant improvements in tumor detection from breast ultrasound (BUSI) images, enhancing the diagnostic accuracy of breast cancer screening. Although deep convolutional neural networks (CNNs) and transformer-based architectures have individually yielded promising results, challenges such as low contrast, spatial variability, and irregular tumor shapes continue to hinder the robustness of current methods. Therefore, in this study, a novel hybrid CNN–Transformer framework is proposed to improve discriminative feature extraction for BUSI cancer analysis. The network employs a dual-branch architecture, integrating features extracted from both CNN and transformer models. In the first branch, the Swin Transformer is combined with a Triplet Attention to strengthen its ability to learn long-range dependencies and global contextual information. The Triple Attention module processes feature maps along three orthogonal axes, enabling a more effective representation of both spatial and channel-level relationships. The second branch incorporates the Efficient Net architecture augmented with an Efficient Channel Attention (ECA) module, which facilitates adaptive channel-level feature recalibration. This design allows the model to emphasize diagnostically salient regions within ultrasound images. High-level features from both branches are fused for final classification. Experimental results on the BUSI dataset demonstrate that the proposed architecture achieves superior performance, with 97.4% accuracy, 97.9% precision, 97.9% sensitivity, and a 97.9% F1-score. These outcomes confirm the effectiveness of the proposed hybrid CNN–Transformer design in improving automated breast cancer diagnosis using ultrasound imaging.

Keywords

Ethical Statement

The study is complied with research and publication ethics.

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other), Signal Processing

Journal Section

Research Article

Authors

Aslı Nur Polat ^*
0000-0001-6262-4968
Türkiye

Hussein Mahmood Abdo Mohammed
0000-0001-5491-5669
Yemen

Publication Date

December 31, 2025

Submission Date

August 25, 2025

Acceptance Date

December 15, 2025

Published in Issue

Year 2025 Volume: 14 Number: 4

DOI

https://doi.org/10.17798/bitlisfen.1772185

IZ

https://izlik.org/JA87KK79YL

Cite

RIS / Bibtex

APA

Polat, A. N., & Mohammed, H. M. A. (2025). Improving Breast Cancer Diagnosis using Attention-Enhanced Hybrid CNN–Transformer Model. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 14(4), 2616-2638. https://doi.org/10.17798/bitlisfen.1772185

AMA

1.Polat AN, Mohammed HMA. Improving Breast Cancer Diagnosis using Attention-Enhanced Hybrid CNN–Transformer Model. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2025;14(4):2616-2638. doi:10.17798/bitlisfen.1772185

Chicago

Polat, Aslı Nur, and Hussein Mahmood Abdo Mohammed. 2025. “Improving Breast Cancer Diagnosis Using Attention-Enhanced Hybrid CNN–Transformer Model”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 14 (4): 2616-38. https://doi.org/10.17798/bitlisfen.1772185.

EndNote

Polat AN, Mohammed HMA (December 1, 2025) Improving Breast Cancer Diagnosis using Attention-Enhanced Hybrid CNN–Transformer Model. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 14 4 2616–2638.

IEEE

[1]A. N. Polat and H. M. A. Mohammed, “Improving Breast Cancer Diagnosis using Attention-Enhanced Hybrid CNN–Transformer Model”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 14, no. 4, pp. 2616–2638, Dec. 2025, doi: 10.17798/bitlisfen.1772185.

ISNAD

Polat, Aslı Nur - Mohammed, Hussein Mahmood Abdo. “Improving Breast Cancer Diagnosis Using Attention-Enhanced Hybrid CNN–Transformer Model”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 14/4 (December 1, 2025): 2616-2638. https://doi.org/10.17798/bitlisfen.1772185.

JAMA

1.Polat AN, Mohammed HMA. Improving Breast Cancer Diagnosis using Attention-Enhanced Hybrid CNN–Transformer Model. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2025;14:2616–2638.

MLA

Polat, Aslı Nur, and Hussein Mahmood Abdo Mohammed. “Improving Breast Cancer Diagnosis Using Attention-Enhanced Hybrid CNN–Transformer Model”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 14, no. 4, Dec. 2025, pp. 2616-38, doi:10.17798/bitlisfen.1772185.

Vancouver

1.Aslı Nur Polat, Hussein Mahmood Abdo Mohammed. Improving Breast Cancer Diagnosis using Attention-Enhanced Hybrid CNN–Transformer Model. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2025 Dec. 1;14(4):2616-38. doi:10.17798/bitlisfen.1772185