AML, ALL, and CML classification and diagnosis based on bone marrow cell morphology combined with convolutional neural network: A STARD compliant diagnosis research.
AML, ALL, and CML classification and diagnosis based on bone marrow cell morphology combined with convolutional neural network: A STARD compliant diagnosis research.
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DOI：
10.1097/md.0000000000023154
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发表时间：
2020-11-06
期刊：
影响因子：
1.6
通讯作者：
Huang W
中科院分区：
文献类型：
--
作者：
Huang F;Guang P;Li F;Liu X;Zhang W;Huang W
基于骨髓细胞形态学的白血病诊断主要依赖于骨髓涂片的人工显微镜。但该方法受主观因素影响较大，容易导致误诊。本研究提出利用骨髓细胞显微镜图像，采用卷积神经网络（CNN）结合迁移学习，建立一种客观、快速、准确的LKA （AML、ALL、CML）分类诊断方法。我们收集了104份骨髓涂片的细胞显微镜图像（包括18名健康受试者，53名AML患者，23名ALL患者和18名CML患者）。首次采用完美反射算法和自适应滤波算法对实验采集的骨髓细胞图像进行预处理。随后，使用3个CNN框架（Inception-V3、ResNet50和DenseNet121）对原始数据集和预处理数据集构建分类模型。利用迁移学习提高模型的预测精度。结果表明，基于预处理数据集的DenseNet121模型分类效果最好，预测准确率为74.8%。迁移学习优化得到的DenseNet121模型的预测精度为95.3%，提高了20.5%。在该模型中，正常组、AML、ALL和CML的预测准确率分别为90%、99%、97%和95%。结果表明，基于CNN结合迁移学习的白血病细胞形态分类诊断是可行的。与传统的人工显微镜相比，该方法快速、准确、客观。
Leukemia diagnosis based on bone marrow cell morphology primarily relies on the manual microscopy of bone marrow smears. However, this method is greatly affected by subjective factors and tends to lead to misdiagnosis. This study proposes using bone marrow cell microscopy images and employs convolutional neural network (CNN) combined with transfer learning to establish an objective, rapid, and accurate method for classification and diagnosis of LKA (AML, ALL, and CML). We collected cell microscopy images of 104 bone marrow smears (including 18 healthy subjects, 53 AML patients, 23 ALL patients, and 18 CML patients). The perfect reflection algorithm and a self-adaptive filter algorithm were first used for preprocessing of bone marrow cell images collected from experiments. Subsequently, 3 CNN frameworks (Inception-V3, ResNet50, and DenseNet121) were used to construct classification models for the raw dataset and preprocessed dataset. Transfer learning was used to improve the prediction accuracy of the model. Results showed that the DenseNet121 model based on the preprocessed dataset provided the best classification results, with a prediction accuracy of 74.8%. The prediction accuracy of the DenseNet121 model that was obtained by transfer learning optimization was 95.3%, which was increased by 20.5%. In this model, the prediction accuracies of the normal groups, AML, ALL, and CML were 90%, 99%, 97%, and 95%, respectively. The results showed that the leukemic cell morphology classification and diagnosis based on CNN combined with transfer learning is feasible. Compared with conventional manual microscopy, this method is more rapid, accurate, and objective.