Convolutional Neural Networks for Recognition of Lymphoblast Cell Images
Convolutional Neural Networks for Recognition of Lymphoblast Cell Images
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DOI：
10.1155/2019/7519603
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发表时间：
2019-01-01
影响因子：
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通讯作者：
Phon-on, Aniruth
中科院分区：
文献类型：
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作者：
Pansombut, Tatdow;Wikaisuksakul, Siripen;Phon-on, Aniruth
本文介绍了 WHO 对急性淋巴细胞白血病 (ALL) 亚型分类的认可。考虑的两种 ALL 亚型是 T 淋巴细胞白血病（T 前）和 B 淋巴细胞白血病（B 前）。它们表现出各种特征，因此很难区分其成熟细胞（淋巴细胞）的亚型。在通用方法中，必须针对这个复杂的特定领域问题精心设计手工功能。通过深度学习方法，可以消除手工制作的特征工程，因为深度学习方法可以通过卷积神经网络（CNN）的多层架构自动执行此任务。在这项工作中，我们实现了 CNN 分类器来探索深度学习方法识别淋巴细胞和所有亚型的可行性，并且该方法以应用手工特征工程的支持向量机 (SVM) 的主导方法为基准。此外，还应用了两种传统的机器学习分类器、多层感知器（MLP）和随机森林进行比较。实验表明，我们的 CNN 分类器在识别正常淋巴细胞和前 B 细胞方面具有更好的性能。这显示了图像分类的巨大潜力，无需特征工程的多个预处理步骤。
This paper presents the recognition for WHO classification of acute lymphoblastic leukaemia (ALL) subtypes. The two ALL subtypes considered are T-lymphoblastic leukaemia (pre-T) and B-lymphoblastic leukaemia (pre-B). They exhibit various characteristics which make it difficult to distinguish between subtypes from their mature cells, lymphocytes. In a common approach, handcrafted features must be well designed for this complex domain-specific problem. With deep learning approach, handcrafted feature engineering can be eliminated because a deep learning method can automate this task through the multilayer architecture of a convolutional neural network (CNN). In this work, we implement a CNN classifier to explore the feasibility of deep learning approach to identify lymphocytes and ALL subtypes, and this approach is benchmarked against a dominant approach of support vector machines (SVMs) applying handcrafted feature engineering. Additionally, two traditional machine learning classifiers, multilayer perceptron (MLP), and random forest are also applied for the comparison. The experiments show that our CNN classifier delivers better performance to identify normal lymphocytes and pre-B cells. This shows a great potential for image classification with no requirement of multiple preprocessing steps from feature engineering.