Kumaraswamy Eelandula, Kumar Sumit, Sharma Manoj
School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara 144411, Punjab, India.
Division of Research & Development, Lovely Professional University, Phagwara 144411, Punjab, India.
Diagnostics (Basel). 2023 Jun 5;13(11):1977. doi: 10.3390/diagnostics13111977.
Invasive Ductal Carcinoma Breast Cancer (IDC-BC) is the most common type of cancer and its asymptomatic nature has led to an increased mortality rate globally. Advancements in artificial intelligence and machine learning have revolutionized the medical field with the development of AI-enabled computer-aided diagnosis (CAD) systems, which help in determining diseases at an early stage. CAD systems assist pathologists in their decision-making process to produce more reliable outcomes in order to treat patients well. In this work, the potential of pre-trained convolutional neural networks (CNNs) (i.e., EfficientNetV2L, ResNet152V2, DenseNet201), singly or as an ensemble, was thoroughly explored. The performances of these models were evaluated for IDC-BC grade classification using the DataBiox dataset. Data augmentation was used to avoid the issues of data scarcity and data imbalances. The performance of the best model was compared to three different balanced datasets of Databiox (i.e., 1200, 1400, and 1600 images) to determine the implications of this data augmentation. Furthermore, the effects of the number of epochs were analysed to ensure the coherency of the most optimal model. The experimental results analysis revealed that the proposed ensemble model outperformed the existing state-of-the-art techniques in relation to classifying the IDC-BC grades of the Databiox dataset. The proposed ensemble model of the CNNs achieved a 94% classification accuracy and attained a significant area under the ROC curves for grades 1, 2, and 3, i.e., 96%, 94%, and 96%, respectively.
浸润性导管癌乳腺癌（IDC-BC）是最常见的癌症类型，其无症状的特性导致全球死亡率上升。人工智能和机器学习的进步通过开发人工智能辅助计算机辅助诊断（CAD）系统彻底改变了医学领域，该系统有助于在早期阶段确定疾病。CAD系统协助病理学家进行决策过程，以产生更可靠的结果，从而更好地治疗患者。在这项工作中，我们深入探索了预训练卷积神经网络（CNN）（即EfficientNetV2L、ResNet152V2、DenseNet201）单独或作为一个集成模型的潜力。使用DataBiox数据集评估这些模型在IDC-BC分级分类方面的性能。采用数据增强来避免数据稀缺和数据不平衡的问题。将最佳模型的性能与Databiox的三个不同平衡数据集（即1200、1400和1600张图像）进行比较，以确定这种数据增强的影响。此外，分析了轮次数量的影响，以确保最优模型的一致性。实验结果分析表明，所提出的集成模型在对Databiox数据集的IDC-BC分级进行分类方面优于现有的先进技术。所提出的CNN集成模型实现了94%的分类准确率，并且在1级、2级和3级的ROC曲线下分别获得了显著的面积，即96%、94%和96%。
