XAI-CAD: towards trustworthy explainable CAD for breast ultrasound diagnosis

Abstract

Purpose: Breast cancer is a major global health concern, with early detection being critical for effective treatment and reduced mortality. While ultrasound imaging is a widely used diagnostic tool, manual interpretation is time-consuming and subject to variability among clinicians. Automated, accurate, and interpretable approaches for segmentation and classification are therefore essential to support clinical decision-making. Methods: This study introduces XAI-CAD (Explainable Artificial Intelligence-based Computer-Aided Diagnosis), a unified and interpretable multitask deep learning framework tailored for breast ultrasound imaging. XAI-CAD combines a U-Net-based segmentation backbone with pretrained encoders for classification, performing lesion segmentation and pathological diagnosis simultaneously. To enhance interpretability, the framework incorporates Grad-CAM and attention-based visualization techniques, highlighting regions most relevant to the classification decision. The system was evaluated on the publicly available Breast Ultrasound Images (BUSI) dataset, with rigorous statistical validation to ensure reliability. Results: The proposed framework achieved over 90% Dice coefficient, precision, and recall for segmentation, and outperformed several recent state-of-the-art models in classification accuracy. Explainability analyzes demonstrated that the model consistently focuses on clinically significant areas, providing transparent decision support. Conclusion: XAI-CAD offers a robust, accurate, and interpretable solution for breast cancer detection in ultrasound images. Its multitask and explainable design presents a clear methodological contribution and can be adapted to other medical imaging modalities, supporting both improved diagnostic performance and clinician trust.