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An Interpretable Ensemble Transformer Framework for Breast Cancer Detection in Ultrasound Images.

Riyadh M Al-Tam¹, Aymen M Al-Hejri^1,2, Fatma A Hashim^3,4

¹Faculty of Administrative and Computer Sciences, University of Albaydha, Albaydha CV46+6X, Yemen.

Diagnostics (Basel, Switzerland)

|February 27, 2026

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This study developed an interpretable computer-aided diagnosis (CAD) system using Vision Transformers for breast cancer detection. The AI model achieved high accuracy, showing promise as a reliable tool for early breast cancer diagnosis.

Area of Science:

Medical Imaging
Artificial Intelligence
Computer-Aided Diagnosis

Background:

Early breast cancer detection is crucial for survival rates.
Manual interpretation of ultrasound images faces challenges like noise and subjectivity.
Automated systems are needed to improve diagnostic accuracy and consistency.

Purpose of the Study:

To develop an automated and interpretable computer-aided diagnosis (CAD) system for breast cancer detection.
To enhance the accuracy and reliability of breast ultrasound image analysis.
To provide visual explanations for clinical interpretability.

Main Methods:

An ensemble transfer learning approach integrating Data-Efficient Image Transformer (Deit) and Vision Transformer (ViT) was developed.

Keywords:

breast cancer detection computer-aided diagnosis (CAD)ensemble learning transfer learning ultrasound imaging vision transformer

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Feature fusion, preprocessing, normalization, and data augmentation were employed to improve model robustness.

Gradient-weighted Class Activation Mapping (Grad-CAM) was used for generating visual explanations.

Main Results:

The ensemble model achieved 96.92% accuracy and 97.10% AUC for binary classification.
External validation on multiple datasets confirmed strong generalizability.
Performance on fine-grained BI-RADS classification was lower, indicating inherent clinical complexity.

Conclusions:

The Vision Transformer-based ensemble system demonstrates high diagnostic accuracy and cross-dataset generalization.
The system offers clinically meaningful explainability, suitable for a reliable second-opinion CAD tool.
This AI tool holds potential for breast cancer diagnosis, especially in resource-limited settings.