Güler Osman
Department of Computer Engineering, Çankırı Karatekin University, Çankırı, Turkey.
J Imaging Inform Med. 2025 Apr 29. doi: 10.1007/s10278-025-01515-5.
Breast ultrasound is a useful and rapid diagnostic tool for the early detection of breast cancer. Artificial intelligence-supported computer-aided decision systems, which assist expert radiologists and clinicians, provide reliable and rapid results. Deep learning methods and techniques are widely used in the field of health for early diagnosis, abnormality detection, and disease diagnosis. Therefore, in this study, a deep ensemble learning model based on Dirichlet distribution using pre-trained transfer learning models for breast cancer classification from ultrasound images is proposed. In the study, experiments were conducted using the Breast Ultrasound Images Dataset (BUSI). The dataset, which had an imbalanced class structure, was balanced using data augmentation techniques. DenseNet201, InceptionV3, VGG16, and ResNet152 models were used for transfer learning with fivefold cross-validation. Statistical analyses, including the ANOVA test and Tukey HSD test, were applied to evaluate the model's performance and ensure the reliability of the results. Additionally, Grad-CAM (Gradient-weighted Class Activation Mapping) was used for explainable AI (XAI), providing visual explanations of the deep learning model's decision-making process. The spaced repetition method, commonly used to improve the success of learners in educational sciences, was adapted to artificial intelligence in this study. The results of training with transfer learning models were used as input for further training, and spaced repetition was applied using previously learned information. The use of the spaced repetition method led to increased model success and reduced learning times. The weights obtained from the trained models were input into an ensemble learning system based on Dirichlet distribution with different variations. The proposed model achieved 99.60% validation accuracy on the dataset, demonstrating its effectiveness in breast cancer classification.
乳腺超声是早期检测乳腺癌的一种有用且快速的诊断工具。人工智能支持的计算机辅助决策系统可协助专家放射科医生和临床医生，能提供可靠且快速的结果。深度学习方法和技术在健康领域被广泛用于早期诊断、异常检测和疾病诊断。因此，在本研究中，提出了一种基于狄利克雷分布的深度集成学习模型，该模型使用预训练的迁移学习模型对超声图像进行乳腺癌分类。在该研究中，使用乳腺超声图像数据集（BUSI）进行了实验。该数据集具有不平衡的类结构，使用数据增强技术进行了平衡。使用DenseNet201、InceptionV3、VGG16和ResNet152模型进行迁移学习，并采用五折交叉验证。应用包括方差分析测试和Tukey HSD测试在内的统计分析来评估模型的性能，并确保结果的可靠性。此外，使用Grad-CAM（梯度加权类激活映射）进行可解释人工智能（XAI），对深度学习模型的决策过程进行可视化解释。在教育科学中常用于提高学习者成功率的间隔重复方法，在本研究中被应用于人工智能。使用迁移学习模型的训练结果作为进一步训练的输入，并利用先前学到的信息应用间隔重复。间隔重复方法的使用提高了模型的成功率并减少了学习时间。从训练模型中获得的权重被输入到基于具有不同变体的狄利克雷分布的集成学习系统中。所提出的模型在数据集上实现了99.60%的验证准确率，证明了其在乳腺癌分类中的有效性。
