Conference Paper2025

The Role of Model Architecture in Breast Cancer Classification: A Comparison of CNN and Transformer Paradigms on Ultrasound Data

Siam Tahsin Bhuiyan, Halima Khatun, Samiul Karim Mazumder, Fatin Israq, Riyadul Islam, Rashedur Rahman, Ashraful Islam, Saadia Binte Alam

2025 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE)

IEEE, pp. 356-361

  • Good Health and Well-being — Relevance: 41%
DOI: 10.1109/wiecon-ece69386.2025.11525925URL

Abstract

Breast cancer is the leading malignancy in women globally, and early detection is critical for improving survival. Deep learning has shown promise in medical image analysis, yet the effects of model architecture remain underexplored. This study conducts a comparative evaluation of four models: DenseNet121 (pure CNN), ConvNeXt (modernized CNN with transformer-inspired design), ViT-B/16 (pure Vision Transformer), and Swin-B (hierarchical transformer with CNN-like inductive biases) for breast ultrasound classification using the public BUSI dataset of 780 images. Results demonstrate that DenseNet121 exhibits limitations due to its reliance on local receptive fields, which restricts its ability to capture distributed pathological features. ConvNeXt outperforms DenseNet121 across all metrics, with 83% accuracy and 91% AUROC, highlighting the benefits of transformer-inspired refinements within CNNs. ViT-B/16 achieves the highest AUROC of 94%, underscoring the strength of global self-attention for capturing distributed pathological cues. Swin-B attains the most balanced performance, with 86% accuracy, 85% recall, and 0.92 AUROC, reflecting the effectiveness of combining global attention with hierarchical locality. These findings suggest a clear performance hierarchy, where hybrid transformer architectures such as Swin-B offer the most clinically reliable balance between sensitivity and specificity. Future research should explore hybrid models that integrate CNN inductive priors with transformer-based global reasoning to further enhance robustness in real-world diagnostic applications.

Keywords

  • DenseNet121
  • breast cancer classification
  • Swin Transformer
  • model architecture comparison
  • breast ultrasound
  • BUSI dataset
  • ConvNeXt
  • CNN-Transformer hybrid
  • vision transformer
  • self-attention

CCDS Authors

Siam Tahsin Bhuiyan
Siam Tahsin Bhuiyan

Siam Tahsin Bhuiyan

  • Intern, CCDS

Bachelors

Independent University, Bangladesh

Published at

Scientific Reports

Halima Khatun
Halima Khatun

Halima Khatun

  • Research Assistant, CCDS

Bachelors

Independent University, Bangladesh

Published at

Scientific Reports

Riyadul Islam
Riyadul Islam

Riyadul Islam

  • Intern, CCDS

Bachelors

Daffodil International University

Published at

Scientific Reports

Md. Rashedur Rahman, D. Eng.
Md. Rashedur Rahman, D. Eng.

Md. Rashedur Rahman, D. Eng.

  • Co-Director, MIRA & NEST Wings

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

CHI, HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

Saadia Binte Alam, PhD
Saadia Binte Alam, PhD

Saadia Binte Alam, PhD

  • Director, MIRA & WiSE Wings

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

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