Breast cancer is the second most prevalent cause of cancer death and the most common malignancy among women, posing a life-threatening risk. Treatment for breast cancer can be highly effective, with a survival chance of 90% or higher, especially when the disease is detected early. This paper introduces a groundbreaking deep U-Net framework for mammography breast cancer images to perform automatic detection of abnormalities. The objective is to provide segmented images that show areas of tumors more accurately than other deep learning techniques. The proposed framework consists of three steps. The first step is image preprocessing using the Li algorithm to minimize the cross-entropy between the foreground and the background, contrast enhancement using contrast-limited adaptive histogram equalization (CLAHE), normalization, and median filtering. The second step involves data augmentation to mitigate overfitting and underfitting, and the final step is implementing a convolutional encoder-decoder network-based U-Net architecture, characterized by high precision in medical image analysis. The framework has been tested on two comprehensive public datasets, namely INbreast and CBIS-DDSM. Several metrics have been adopted for quantitative performance assessment, including the Dice score, sensitivity, Hausdorff distance, Jaccard coefficient, precision, and F1 score. Quantitative results on the INbreast dataset show an average Dice score of 85.61% and a sensitivity of 81.26%. On the CBIS-DDSM dataset, the average Dice score is 87.98%, and the sensitivity reaches 90.58%. The experimental results ensure earlier and more accurate abnormality detection. Furthermore, the success of the proposed deep learning framework in mammography shows promise for broader applications in medical imaging, potentially revolutionizing various radiological practices.

乳腺癌是癌症死亡的第二大常见原因，也是女性中最常见的恶性肿瘤，构成危及生命的风险。乳腺癌的治疗非常有效，生存率高达 90% 或更高，尤其是在早期发现疾病的情况下。本文介绍了一种突破性的深度 U-Net 框架，用于乳房 X 光检查乳腺癌图像，以执行异常的自动检测。目标是提供比其他深度学习技术更准确地显示肿瘤区域的分割图像。拟议的框架由三个步骤组成。第一步是使用 Li 算法进行图像预处理，以最小化前景和背景之间的交叉熵，使用对比度限制自适应直方图均衡 (CLAHE) 进行对比度增强、归一化和中值滤波。第二步涉及数据增强，以减轻过度拟合和欠拟合，最后一步是实现基于卷积编码器-解码器网络的 U-Net 架构，其特点是医学图像分析的高精度。该框架已在两个综合公共数据集（INbreast 和 CBIS-DDSM）上进行了测试。定量绩效评估采用了多种指标，包括 Dice 分数、灵敏度、豪斯多夫距离、Jaccard 系数、精度和 F1 分数。 INbreast 数据集的定量结果显示平均 Dice 得分为 85.61%，敏感性为 81.26%。在CBIS-DDSM数据集上，平均Dice得分为87.98%，灵敏度达到90.58%。实验结果确保了更早、更准确的异常检测。 此外，所提出的乳房X光检查深度学习框架的成功表明了其在医学成像领域更广泛应用的前景，有可能彻底改变各种放射学实践。