An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification.
An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification.
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DOI：
10.3390/diagnostics10121064
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发表时间：
2020-12-08
期刊：
影响因子：
--
通讯作者：
Jang JW
中科院分区：
文献类型：
--
作者：
Kasani PH;Park SW;Jang JW
白血病是一种骨髓中的血细胞癌，对儿童和青少年都有影响。异常淋巴细胞的快速生长导致骨髓衰竭，这可能会减缓新血细胞的产生，从而增加患者的发病率和死亡率。年龄是白血病诊断的关键临床因素，因为如果白血病在早期被诊断出来，它是高度可治愈的。全球的发病率正在上升，因为全世界约有41.2万人可能被诊断为某种类型的白血病，其中急性淋巴细胞性白血病约占全球所有白血病病例的12%。因此，对正常和恶性细胞的可靠和准确的检测是非常重要的。计算机辅助诊断(CAD)模型的自动检测可以帮助医务人员，并有助于白血病的早期发现。本文以单中心研究为基础，旨在构建一种用于白血病B淋巴母细胞分类的聚集型深度学习模型。为了获得可靠、准确的深度学习器，针对有限的数据集，采用数据扩充技术，并采用迁移学习策略加快学习过程，进一步提高了网络的性能。结果表明，我们提出的方法能够融合从最佳深度学习模型中提取的特征，并且在白血病B淋巴母细胞诊断中的测试准确率为96.58%，优于单个网络。
Leukemia is a cancer of blood cells in the bone marrow that affects both children and adolescents. The rapid growth of unusual lymphocyte cells leads to bone marrow failure, which may slow down the production of new blood cells, and hence increases patient morbidity and mortality. Age is a crucial clinical factor in leukemia diagnosis, since if leukemia is diagnosed in the early stages, it is highly curable. Incidence is increasing globally, as around 412,000 people worldwide are likely to be diagnosed with some type of leukemia, of which acute lymphoblastic leukemia accounts for approximately 12% of all leukemia cases worldwide. Thus, the reliable and accurate detection of normal and malignant cells is of major interest. Automatic detection with computer-aided diagnosis (CAD) models can assist medics, and can be beneficial for the early detection of leukemia. In this paper, a single center study, we aimed to build an aggregated deep learning model for Leukemic B-lymphoblast classification. To make a reliable and accurate deep learner, data augmentation techniques were applied to tackle the limited dataset size, and a transfer learning strategy was employed to accelerate the learning process, and further improve the performance of the proposed network. The results show that our proposed approach was able to fuse features extracted from the best deep learning models, and outperformed individual networks with a test accuracy of 96.58% in Leukemic B-lymphoblast diagnosis.
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DOI：
10.5391/ijfis.2016.16.2.125
发表时间：
2016-06-30
影响因子：
1.3
作者：
Kassani, Peyman Hosseinzadeh;Kim, Euntai
通讯作者：
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作者：
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影响因子：
4.5
作者：
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通讯作者：
Friedman, JH
DOI：
10.1016/j.htct.2018.05.010
发表时间：
2019-03-01
期刊：
Hematology, Transfusion and Cell Therapy
影响因子：
--
作者：
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通讯作者：
Gómez-Almaguer, David