Breast cancer has been one of the main causes of death among women recently, and it has been the focus of attention of many specialists and researchers in the health field. Because of its seriousness and spread speed, breast cancer-resisting methods, early diagnosis, diagnosis, and treatment have been the points of research discussion. Many computers-aided diagnosis (CAD) systems have been proposed to reduce the load on physicians and increase the accuracy of breast tumor diagnosis. To the best of our knowledge, combining patient information, including medical history, breast density, age, and other factors, with mammogram features from both breasts in craniocaudal (CC) and mediolateral oblique (MLO) views has not been previously investigated for breast tumor classification. In this paper, we investigated the effectiveness of using those inputs by comparing two combination approaches. The soft voting approach, produced from statistical information-based models (decision tree, random forest, K-nearest neighbor, Gaussian naive Bayes, gradient boosting, and MLP) and an image-based model (CNN), achieved 90% accuracy. Additionally, concatenating statistical and image-based features in a deep learning model achieved 93% accuracy. We found that it produced promising results that would enhance the CAD systems. As a result, this study finds that using both sides of mammograms outperformed the result of using only the infected side. In addition, integrating the mammogram features with statistical information enhanced the accuracy of the tumor classification. Our findings, based on a novel dataset, incorporate both patient information and four-view mammogram images, covering multiple classes: normal, benign, and malignant.
近年来，乳腺癌已成为女性主要死因之一，受到健康领域众多专家与研究者的重点关注。鉴于其严重性与扩散速度，乳腺癌的防治方法、早期诊断及治疗方案已成为研究讨论的核心议题。为减轻医师负担并提高乳腺肿瘤诊断准确性，多种计算机辅助诊断系统应运而生。据我们所知，将患者病史、乳腺密度、年龄等信息与双侧乳腺头尾位及内外斜位影像特征相结合用于乳腺肿瘤分类的研究尚未见报道。本研究通过比较两种融合策略，探讨了多源数据整合的有效性：基于统计信息模型与影像特征模型的软投票策略实现了90%的准确率；而在深度学习模型中融合统计特征与影像特征的方法则达到93%的准确率。实验表明，双侧乳腺影像联合分析优于仅采用患侧影像的结果，且影像特征与统计信息的整合显著提升了肿瘤分类精度。本研究基于包含患者信息与四视图乳腺影像的新型数据集展开，涵盖正常、良性、恶性三类样本，其发现将为计算机辅助诊断系统的优化提供重要参考。
An Integrated Multimodal-Based CAD System for Breast Cancer Diagnosis
肿瘤(癌症)患者之家