Breast cancer (BCa) poses a severe threat to women’s health worldwide as it is the most frequently diagnosed type of cancer and the primary cause of death for female patients. The biopsy procedure remains the gold standard for accurate and effective diagnosis of BCa. However, its adverse effects, such as invasiveness, bleeding, infection, and reporting time, keep this procedure as a last resort for diagnosis. A mammogram is considered the routine noninvasive imaging-based procedure for diagnosing BCa, mitigating the need for biopsies; however, it might be prone to subjectivity depending on the radiologist’s experience. Therefore, we propose a novel, mammogram image-based BCa explainable AI (BCaXAI) model with a deep learning-based framework for precise, noninvasive, objective, and timely manner diagnosis of BCa. The proposed BCaXAI leverages the Inception-ResNet V2 architecture, where the integration of explainable AI components, such as Grad-CAM, provides radiologists with valuable visual insights into the model’s decision-making process, fostering trust and confidence in the AI-based system. Based on using the DDSM and CBIS-DDSM mammogram datasets, BCaXAI achieved exceptional performance, surpassing traditional models such as ResNet50 and VGG16. The model demonstrated superior accuracy (98.53%), recall (98.53%), precision (98.40%), F1-score (98.43%), and AUROC (0.9933), highlighting its effectiveness in distinguishing between benign and malignant cases. These promising results could alleviate the diagnostic subjectivity that might arise as a result of the experience-variability between different radiologists, as well as minimize the need for repetitive biopsy procedures.
乳腺癌作为全球女性中最常被诊断出的癌症类型及主要致死原因，对女性健康构成严重威胁。活检技术目前仍是实现乳腺癌精准有效诊断的金标准，但其侵入性操作可能引发的出血、感染等副作用以及较长的报告周期，使其通常仅作为最终诊断手段。乳腺X线摄影作为常规无创影像学检查方法，可在一定程度上减少活检需求，但其诊断结果易受放射科医师经验差异带来的主观性影响。为此，我们提出一种基于乳腺X线影像的新型可解释人工智能乳腺癌诊断模型（BCaXAI），该模型采用深度学习框架，能够实现精准、无创、客观且高效的乳腺癌诊断。BCaXAI模型采用Inception-ResNet V2架构，通过集成梯度加权类激活映射等可解释人工智能组件，为放射科医师提供直观的决策过程可视化分析，从而增强对人工智能系统的信任度。基于DDSM和CBIS-DDSM乳腺X线影像数据集的验证表明，BCaXAI模型在多项性能指标上均显著优于ResNet50、VGG16等传统模型，展现出卓越的诊断效能：准确率达98.53%、召回率98.53%、精确率98.40%、F1分数98.43%、受试者工作特征曲线下面积达0.9933。这些突出成果不仅有助于缓解因放射科医师经验差异导致的诊断主观性问题，还能有效减少重复性活检操作的需求。
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