Department of Mathematics Technical University of Catalonia Barcelona East Engineering School, Spain; Biomedic Diagnostic Center, Clinic Hospital of Barcelona, University of Barcelona, Spain.
Department of Mathematics Technical University of Catalonia Barcelona East Engineering School, Spain.
Comput Methods Programs Biomed. 2019 Oct;180:105020. doi: 10.1016/j.cmpb.2019.105020. Epub 2019 Aug 9.
Morphological analysis is the starting point for the diagnostic approach of more than 80% of hematological diseases. However, the morphological differentiation among different types of normal and abnormal peripheral blood cells is a difficult task that requires experience and skills. Therefore, the paper proposes a system for the automatic classification of eight groups of peripheral blood cells with high accuracy by means of a transfer learning approach using convolutional neural networks. With this new approach, it is not necessary to implement image segmentation, the feature extraction becomes automatic and existing models can be fine-tuned to obtain specific classifiers.
A dataset of 17,092 images of eight classes of normal peripheral blood cells was acquired using the CellaVision DM96 analyzer. All images were identified by pathologists as the ground truth to train a model to classify different cell types: neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (myelocytes, metamyelocytes and promyelocytes), erythroblasts and platelets. Two designs were performed based on two architectures of convolutional neural networks, Vgg-16 and Inceptionv3. In the first case, the networks were used as feature extractors and these features were used to train a support vector machine classifier. In the second case, the same networks were fine-tuned with our dataset to obtain two end-to-end models for classification of the eight classes of blood cells.
In the first case, the experimental test accuracies obtained were 86% and 90% when extracting features with Vgg-16 and Inceptionv3, respectively. On the other hand, in the fine-tuning experiment, global accuracy values of 96% and 95% were obtained using Vgg-16 and Inceptionv3, respectively. All the models were trained and tested using Keras and Tensorflow with a Nvidia Titan XP Graphics Processing Unit.
The main contribution of this paper is a classification scheme involving a convolutional neural network trained to discriminate among eight classes of cells circulating in peripheral blood. Starting from a state-of-the-art general architecture, we have established a fine-tuning procedure to develop an end-to-end classifier trained using a dataset with over 17,000 cell images obtained from clinical practice. The performance obtained when testing the system has been truly satisfactory, the values of precision, sensitivity, and specificity being excellent. To summarize, the best overall classification accuracy has been 96.2%.
形态学分析是超过 80%的血液系统疾病诊断方法的起点。然而，不同类型的正常和异常外周血细胞之间的形态学分化是一项需要经验和技能的艰巨任务。因此，本文提出了一种通过使用卷积神经网络的迁移学习方法对八组外周血细胞进行自动分类的系统，该系统的准确率非常高。通过这种新方法，不需要进行图像分割，特征提取变得自动化，并且可以对现有模型进行微调以获得特定的分类器。
使用 CellaVision DM96 分析仪采集了 17092 张正常外周血八类细胞的图像数据集。所有图像均由病理学家识别为真实数据，以训练一个模型来分类不同的细胞类型：中性粒细胞、嗜酸性粒细胞、嗜碱性粒细胞、淋巴细胞、单核细胞、未成熟粒细胞（髓细胞、中幼粒细胞和早幼粒细胞）、成红细胞和血小板。基于卷积神经网络的两种架构（Vgg-16 和 Inceptionv3）进行了两项设计。在第一种情况下，将网络用作特征提取器，并使用这些特征来训练支持向量机分类器。在第二种情况下，使用我们的数据集对相同的网络进行微调，以获得用于分类这八类血细胞的两个端到端模型。
在第一种情况下，分别使用 Vgg-16 和 Inceptionv3 提取特征时，实验测试准确率分别为 86%和 90%。另一方面，在微调实验中，分别使用 Vgg-16 和 Inceptionv3 获得了 96%和 95%的总体准确率。所有模型均使用 Keras 和 Tensorflow 与 Nvidia Titan XP 图形处理单元进行训练和测试。
本文的主要贡献是提出了一种涉及使用经过训练以区分外周血中循环的八类细胞的卷积神经网络的分类方案。从最先进的通用架构开始，我们已经建立了一种微调过程，以使用从临床实践中获得的超过 17000 个细胞图像的数据集来开发端到端分类器。在测试系统时获得的性能非常令人满意，精度、灵敏度和特异性的值都非常出色。概括地说，最佳的总体分类准确率为 96.2%。
