Paper Info
Title
A classification method to classify bone marrow cells with class imbalance problem
Abstract
Bone marrow cell morphology has long been used to diagnose blood diseases. However, it requires long-term experience from a suitable person. Furthermore, the outcomes of their recognition are subjective and no quantitative standard has been established yet. Consequently, developing a deep learning automatic system for classifying bone marrow cells is extremely important. However, real-life data sets, such as bone marrow cell data, constantly suffer from a long-tail distribution problem, owing to which the final trained classifier is biased toward a large number of categories. Thus, addressing this issue is crucial. The current research presents a class balance classification method (CBCM) for classifying 15 types of bone marrow cell data sets with a class imbalance problem. CBCM outperforms other balance approaches such as random over-sampling, synthetic minority over-sampling technique (SMOTE), random under-sampling, weighted random forest and weighted cross-entropy function, achieving precision, sensitivity, and specificity values of 84.53%, 84.44% and 99.29% respectively. A more extensive comparison between the baseline and CBCM, as well as the Grad-CAM and Guided Grad-CAM of CBCM, reveals that CBCM is a reliable and effective solution to address the long-tail distribution problem of the bone marrow cell data sets.
Year
DOI
Venue
2022
10.1016/j.bspc.2021.103296
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Keywords
DocType
Volume
Deep learning, Bone marrow cell classification, Class-balanced method, Long-tail distribution
Journal
72
Issue
ISSN
Citations 
Part
1746-8094
0
PageRank 
References 
Authors
0.34
0
10
Name
Order
Citations
PageRank
Liang Guo100.34
Peiduo Huang200.34
Dehao Huang300.34
Zilan Li400.34
Chenglong She500.34
Qianhang Guo600.34
Qingmao Zhang700.34
Jiaming Li800.34
Qiongxiong Ma900.34
Jie Li1000.34