A classification method to classify bone marrow cells with class imbalance problem. (February 2022)

• Record Type: Journal Article
• Authors: Guo, Liang; Huang, Peiduo; Huang, Dehao; Li, Zilan; She, Chenglong; Guo, Qianhang; Zhang, Qingmao; Li, Jiaming; Ma, Qiongxiong; Li, Jie
• Is Part Of: Biomedical signal processing and control. Volume 72(2022)Part A
• Publication Date: 2022-02
• Subjects: Deep learning -- Bone marrow cell classification -- Class-balanced method -- Long-tail distribution; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103296
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: This research presents a class balance classification method (CBCM) for classifying bone marrow cell data sets that have a problem of imbalance. Automatic classifier for identifying 15 different types of bone marrow cells. CBCM uses Class-Balanced focal loss. 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. 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.