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Hybrid Inception v3 XGBoost Model for Acute Lymphoblastic Leukemia Classification
Computational and Mathematical Methods in Medicine ( IF 2.809 ) Pub Date : 2021-07-24 , DOI: 10.1155/2021/2577375
S. Ramaneswaran, Kathiravan Srinivasan, P. M. Durai Raj Vincent, Chuan-Yu Chang

Acute lymphoblastic leukemia (ALL) is the most common type of pediatric malignancy which accounts for 25% of all pediatric cancers. It is a life-threatening disease which if left untreated can cause death within a few weeks. Many computerized methods have been proposed for the detection of ALL from microscopic cell images. In this paper, we propose a hybrid Inception v3 XGBoost model for the classification of acute lymphoblastic leukemia (ALL) from microscopic white blood cell images. In the proposed model, Inception v3 acts as the image feature extractor and the XGBoost model acts as the classification head. Experiments indicate that the proposed model performs better than the other methods identified in literature. The proposed hybrid model achieves a weighted F1 score of 0.986. Through experiments, we demonstrate that using an XGBoost classification head instead of a softmax classification head improves classification performance for this dataset for several different CNN backbones (feature extractors). We also visualize the attention map of the features extracted by Inception v3 to interpret the features learnt by the proposed model.

中文翻译:

用于急性淋巴细胞白血病分类的混合 Inception v3 XGBoost 模型

急性淋巴细胞白血病 (ALL) 是最常见的儿科恶性肿瘤,占所有儿科癌症的 25%。这是一种危及生命的疾病,如果不及时治疗,可能会在几周内导致死亡。已经提出了许多计算机化方法来从显微细胞图像中检测 ALL。在本文中,我们提出了一种混合 Inception v3 XGBoost 模型,用于从显微白细胞图像中对急性淋巴细胞白血病 (ALL) 进行分类。在提出的模型中,Inception v3 作为图像特征提取器,XGBoost 模型作为分类头。实验表明,所提出的模型比文献中确定的其他方法表现更好。所提出的混合模型实现了 0.986 的加权 F1 分数。通过实验,我们证明了使用 XGBoost 分类头而不是 softmax 分类头可以提高该数据集的几个不同 CNN 主干(特征提取器)的分类性能。我们还将 Inception v3 提取的特征的注意力图可视化,以解释所提出模型学习的特征。
更新日期:2021-07-24
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