Breast cancer, with its high mortality, faces diagnostic challenges due to variability in mammography quality and breast densities, leading to inconsistencies in radiological interpretations. Computer-aided diagnostic (CAD) systems, while helpful, struggle with accurately interpreting lesion characteristics such as morphology, density, and size. To address this, our study developed advanced deep-learning algorithms to improve the detection, localization, risk assessment, and classification of breast lesions, aiming to reduce false positives on human intervention and tackle slow convergence rates. Key innovations of the approach include preprocessing techniques, advanced filtering, and data augmentation strategies to optimize model performance, mitigating over- and under-fitting concerns. A significant development is the Chaotic Leader Selective Filler Swarm Optimization (cLSFSO) method, which effectively detects breast-dense lesions by extracting textural and statistical features. Additionally, the study adapted deep learning models like modified VGGNet and SE-ResNet152 through transfer learning, significantly enhancing their capability to distinguish between normal and suspicious mammography regions. The study also introduces hybrid deep neural network-based approaches, including CNN+LSTM and CNN+SVM, for diagnosing and grading cancerous polyps from the pre-segmented ROIs. Besides, the transfer learning paradigm is employed to boost the efficacy in classifying breast masses and reducing computing time by modifying the final layer of the proposed pre-trained models. The integration of Grad-CAM techniques further refines our analysis, leading to more accurate assessments and improved diagnosis of breast anomalies. Evaluated using benchmark and private datasets, our algorithms demonstrated a sensitivity of 0.99 and an overall AUC of 0.99, indicating significant improvements in mammogram analysis. These advancements aid radiologists, potentially improving patient outcomes and contributing to medical imaging and AI in healthcare.

中文翻译：

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利用放射组学和深度学习的力量，通过多参数乳腺钼靶摄影改进乳腺癌诊断

乳腺癌死亡率高，由于乳房X线照相质量和乳腺密度的变化，导致放射学解释不一致，因此面临着诊断挑战。计算机辅助诊断 (CAD) 系统虽然有帮助，但难以准确解释病变特征，例如形态、密度和大小。为了解决这个问题，我们的研究开发了先进的深度学习算法来改进乳腺病变的检测、定位、风险评估和分类，旨在减少人为干预的误报并解决收敛速度慢的问题。该方法的主要创新包括预处理技术、高级过滤和数据增强策略，以优化模型性能，减轻过度拟合和拟合不足的问题。一项重大进展是混沌领导者选择性填充群优化 (cLSFSO) 方法，该方法通过提取纹理和统计特征来有效检测乳房致密病变。此外，该研究通过迁移学习采用了修改后的 VGGNet 和 SE-ResNet152 等深度学习模型，显着增强了它们区分正常和可疑乳房 X 线摄影区域的能力。该研究还引入了基于混合深度神经网络的方法，包括 CNN+LSTM 和 CNN+SVM，用于根据预先分割的 ROI 对癌性息肉进行诊断和分级。此外，通过修改所提出的预训练模型的最后一层，采用迁移学习范式来提高乳腺肿块分类的效率并减少计算时间。 Grad-CAM 技术的集成进一步完善了我们的分析，从而实现更准确的评估并改进对乳房异常的诊断。使用基准和私人数据集进行评估，我们的算法表现出 0.99 的灵敏度和 0.99 的总体 AUC，表明乳房 X 光检查分析的显着改进。这些进步为放射科医生提供帮助，有可能改善患者的治疗结果，并为医疗保健领域的医学成像和人工智能做出贡献。