乔治·TY -的A2 Pennazza盟——Pattanaik Raj Kumar盟——Mishra Satyasis AU -克,默罕默德盟——Gopikrishna Tiruveedula盟——Satapathy萨尼塔PY - 2022 DA - 2022/12/31 TI -乳腺癌分类使用极端的基于机器学习从乳房x光成像DenseNet121模型SP - 2731364六世- 2022 AB -乳腺癌的特点是异常不连续的衬里细胞女人的乳导管。大量的妇女死于乳腺癌的症状在乳导管。如果早期诊断,可以降低死亡率。放射科医生或内科医生,手动分析乳腺癌的乳房x光检查图像成为耗时。防止手工分析和简化的工作分类,介绍了一种新颖的混合DenseNet121-based极端学习机模型(ELM)分类乳腺癌的乳房x光成像。乳房x光图像处理通过预处理和数据增加的阶段。的特性分别收集池和压平层后的第一阶段分类。此外,功能是作为输入提供给DenseNet121-ELM模型提出的完全连接层作为输入。一个极端学习机模型已经取代完全连接层。极端的学习机器的权重更新了AdaGrad优化算法来提高模型的鲁棒性和性能。 Due to its faster convergence speed than other optimization techniques, the AdaGrad algorithm optimization was chosen. In this research, the Digital Database for Screening Mammography (DDSM) dataset mammogram images were utilized, and the results are presented. We have considered the batch size of 32, 64, and 128 for the performance measure, accuracy, sensitivity, specificity, and computational time. The proposed DenseNet121+ELM model achieves 99.47% and 99.14% as training accuracy and testing accuracy for batch size 128. Also, it achieves specificity, sensitivity, and computational time of 99.37%, 99.94%, and 159.7731 minutes, respectively. Further, the comparison result of performance measures is presented for batch sizes 32, 64, and 128 to show the robustness of the proposed DenseNet121+ELM model. The automatic classification performance of the DenseNet121+ELM model has much potential to be applied to the clinical diagnosis of breast cancer. SN - 1687-725X UR - https://doi.org/10.1155/2022/2731364 DO - 10.1155/2022/2731364 JF - Journal of Sensors PB - Hindawi KW - ER -