Kasani Payam Hosseinzadeh, Park Sang-Won, Jang Jae-Won
Department of Neurology, Kangwon National University Hospital, Chuncheon 24289, Korea.
Department of Medical Bigdata Convergence, Kangwon National University, Chuncheon 24289, Korea.
Diagnostics (Basel). 2020 Dec 8;10(12):1064. doi: 10.3390/diagnostics10121064.
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.
白血病是一种影响儿童和青少年的骨髓血细胞癌症。异常淋巴细胞的快速生长会导致骨髓衰竭，这可能会减缓新血细胞的生成，从而增加患者的发病率和死亡率。年龄是白血病诊断中的一个关键临床因素，因为如果白血病在早期被诊断出来，治愈率很高。全球范围内白血病的发病率正在上升，全世界约有41.2万人可能被诊断出患有某种类型的白血病，其中急性淋巴细胞白血病约占全球所有白血病病例的12%。因此，可靠且准确地检测正常细胞和恶性细胞备受关注。利用计算机辅助诊断（CAD）模型进行自动检测可以帮助医务人员，并且有利于白血病的早期检测。在本文的一项单中心研究中，我们旨在构建一个用于白血病B淋巴细胞母细胞分类的聚合深度学习模型。为了创建一个可靠且准确的深度学习者，我们应用了数据增强技术来处理有限的数据集规模，并采用迁移学习策略来加速学习过程，进而提高所提出网络的性能。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，在白血病B淋巴细胞母细胞诊断中以96.58%的测试准确率优于单个网络。
