Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models
Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models
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DOI：
10.3390/s20164373
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发表时间：
2020-08-01
期刊：
影响因子：
3.9
通讯作者：
Maria Vanegas, Ana
中科院分区：
文献类型：
--
作者：
Hameed, Zabit;Zahia, Sofia;Maria Vanegas, Ana
乳腺癌是主要的公共卫生问题之一，被认为是全世界妇女癌症相关死亡的主要原因。其早期诊断可以有效地帮助提高生存率的机会。为此，活检通常作为金标准方法，其中收集组织用于显微镜分析。然而，乳腺癌的组织病理学分析是不平凡的，劳动密集型的，并可能导致病理学家之间的高度分歧。因此，自动诊断系统可以帮助病理学家提高诊断过程的有效性。本文提出了一种集成深度学习方法，用于使用我们收集的数据集对非癌和癌乳腺癌组织病理学图像进行明确分类。我们基于预训练的VGG 16和VGG 19架构训练了四个不同的模型。最初，我们对所有单个模型进行了5重交叉验证操作，即完全训练的VGG 16，微调的VGG 16，完全训练的VGG 19和微调的VGG 19模型。然后，我们采用了一种集成策略，取预测概率的平均值，发现微调的VGG 16和微调的VGG 19的集成具有竞争性的分类性能，尤其是在癌症类别上。微调VGG 16和VGG 19模型的集成提供了97.73%的癌症分类的敏感性和95.29%的总体准确性。此外，它提供了95.29%的F1分数。这些实验结果表明，我们提出的深度学习方法对于乳腺癌的复杂组织病理学图像的自动分类是有效的，更具体地说，对于癌症图像。
Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usually followed as a gold standard approach in which tissues are collected for microscopic analysis. However, the histopathological analysis of breast cancer is non-trivial, labor-intensive, and may lead to a high degree of disagreement among pathologists. Therefore, an automatic diagnostic system could assist pathologists to improve the effectiveness of diagnostic processes. This paper presents an ensemble deep learning approach for the definite classification of non-carcinoma and carcinoma breast cancer histopathology images using our collected dataset. We trained four different models based on pre-trained VGG16 and VGG19 architectures. Initially, we followed 5-fold cross-validation operations on all the individual models, namely, fully-trained VGG16, fine-tuned VGG16, fully-trained VGG19, and fine-tuned VGG19 models. Then, we followed an ensemble strategy by taking the average of predicted probabilities and found that the ensemble of fine-tuned VGG16 and fine-tuned VGG19 performed competitive classification performance, especially on the carcinoma class. The ensemble of fine-tuned VGG16 and VGG19 models offered sensitivity of97.73%for carcinoma class and overall accuracy of95.29%. Also, it offered an F1 score of95.29%. These experimental results demonstrated that our proposed deep learning approach is effective for the automatic classification of complex-natured histopathology images of breast cancer, more specifically for carcinoma images.