■乳腺癌仍然是一个紧迫的全球健康问题,需要进行准确的诊断才能进行有效的干预。深度学习模型(AlexNet、ResNet-50,VGG16,GoogLeNet)显示出显着的微钙化鉴定(>90%)。然而,不同的架构和方法带来了挑战。我们提出了一个集成模型,合并独特的视角,提高精度,了解乳腺癌干预的关键因素。评估支持GoogleNet和ResNet-50,推动他们选择组合功能,确保提高精度,和临床环境中微钙化检测的可靠性。
■本研究提出了一个全面的乳房X线照片预处理框架,使用优化的深度学习集成方法。所提出的框架从使用Otsu分割和形态学操作的伪影去除开始。后续步骤包括调整图像大小,自适应中值滤波,以及通过ResNet-50模型的迁移学习开发深度卷积神经网络(D-CNN)。超参数优化,和集成优化(AlexNet,GoogLeNet,VGG16,ResNet-50)用于识别微钙化的局部区域。严格的评估方案验证了单个模型的有效性,最终在集成模型中展示出较高的预测准确性。
■根据我们的分析,提出的集成模型在微钙化分类中表现出卓越的性能。模型的平均置信度得分证明了这一点,这表明在区分这些关键特性方面具有高度的可靠性和确定性。所提出的模型在微钙化的分类中表现出值得注意的平均置信水平为0.9305。优于替代模型,并为模型的可靠性提供了实质性的见解。集成模型对正常病例分类的平均置信度为0.8859,这增强了模型的一致性和可靠性预测。此外,集成模型在准确性方面获得了非常高的性能,精度,召回,F1分数,和曲线下面积(AUC)。
■所提出的模型对数据集进行了彻底的整合,并专注于班级内的平均置信度等级,从而提高了乳腺癌的临床诊断准确性和有效性。这项研究引入了一种新颖的方法,利用集成模型和严格的评估标准,大大提高了乳腺癌诊断的准确性和可靠性。特别是在微钙化的检测中。
UNASSIGNED: Breast cancer remains a pressing global health concern, necessitating accurate diagnostics for effective interventions. Deep learning models (AlexNet, ResNet-50, VGG16, GoogLeNet) show remarkable microcalcification identification (>90%). However, distinct architectures and methodologies pose challenges. We propose an
ensemble model, merging unique perspectives, enhancing precision, and understanding critical factors for breast cancer intervention. Evaluation favors GoogleNet and ResNet-50, driving their selection for combined functionalities, ensuring improved precision, and dependability in microcalcification detection in clinical settings.
UNASSIGNED: This study presents a comprehensive mammogram preprocessing framework using an optimized deep learning ensemble approach. The proposed framework begins with artifact removal using Otsu Segmentation and morphological operation. Subsequent steps include image resizing, adaptive median filtering, and deep convolutional neural network (D-CNN) development via transfer learning with ResNet-50 model. Hyperparameters are optimized, and ensemble optimization (AlexNet, GoogLeNet, VGG16, ResNet-50) are constructed to identify the localized area of microcalcification. Rigorous evaluation protocol validates the efficacy of individual models, culminating in the
ensemble model demonstrating superior predictive accuracy.
UNASSIGNED: Based on our analysis, the proposed
ensemble model exhibited exceptional performance in the classification of microcalcifications. This was evidenced by the model\'s average confidence score, which indicated a high degree of dependability and certainty in differentiating these critical characteristics. The proposed model demonstrated a noteworthy average confidence level of 0.9305 in the classification of microcalcification, outperforming alternative models and providing substantial insights into the dependability of the model. The average confidence of the
ensemble model in classifying normal cases was 0.8859, which strengthened the model\'s consistent and dependable predictions. In addition, the
ensemble models attained remarkably high performances in terms of accuracy, precision, recall, F1-score, and area under the curve (AUC).
UNASSIGNED: The proposed model\'s thorough dataset integration and focus on average confidence ratings within classes improve clinical diagnosis accuracy and effectiveness for breast cancer. This study introduces a novel methodology that takes advantage of an ensemble model and rigorous evaluation standards to substantially improve the accuracy and dependability of breast cancer diagnostics, specifically in the detection of microcalcifications.