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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 6
1880-1886

Automatic classification of leukemia subtypes based on multi-scale multi-feature hybrid model

Gao Mingyang1
Geng Yan2
Yu Xiao3
Pei Bo4
Zhao Juanjuan1
Qiang Yan1,5
1. College of Computer Science & Technology(College of Data Science), Taiyuan University of Technology, Jinzhong Shanxi 030600, China
2. School of Basic Medical Sciences, Shanxi Medical University, Taiyuan 030001, China
3. The First Hospital of Shanxi Medical University, Taiyuan 030012, China
4. University of South Florida, Florida, FL 33620, USA
5. School of Software, North University of China, Taiyuan 030051, China
DOI: 10.19734/j.issn.1001-3695.2024.08.0354

Abstract

Leukemia, a highly concealed cancer, presents significant challenges in early detection, making it a focal point for medical professionals. Existing fine-grained classification models struggle with small sample imbalanced datasets, leading to poor performance in classifying leukemia subtypes. To address these issues and accelerate doctors'diagnostic speed while shortening treatment time, this paper proposed a multi-scale multi-feature hybrid model(MSMFHM) for the automatic classification of leukemia subtypes applicable to small sample datasets. The model firstly extracted multi-level structural features from images using a multi-scale feature extraction framework combined with scaling operations and a CNN backbone. Next, a multi-scale fusion module with attention mechanisms integrated these multi-level structural features and extracted fine-grained features, effectively leveraging the robustness of CNN inductive bias and the complex global modeling capabilities of Transfor-mers. Finally, to enhance robustness and mitigate overfitting issues caused by small samples, a multi-feature hybrid module combined texture features with fine-grained features before classification. A dataset of 7 156 leukemia cell images, along with other relevant public datasets, was collected to evaluate this method. The proposed model achieves classification accuracies of 93.03% and 99.42% on private and public datasets, respectively, outperforming other advanced models. This method accurately distinguished acute leukemia subtype cells and serves as an effective design approach for computer-aided diagnosis of leukemia.

Foundation Support

国家自然科学基金合作项目(U21A20469)
国家自然科学基金资助项目(62376183)
中央引导地方科技发展资金资助项目(YDZJSX2022C004)
山西省科技创新人才团队专项资助项目(202304051001009)
山西省大健康产业高质量发展科研专项课题(DJKZXKT2023008)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.08.0354
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 6
Section: Technology of Graphic & Image
Pages: 1880-1886
Serial Number: 1001-3695(2025)06-038-1880-07

Publish History

[2025-03-06] Accepted Paper
[2025-06-05] Printed Article

Cite This Article

高铭阳, 耿燕, 尉骁, 等. 基于多级多特征混合模型的白血病亚型自动分类 [J]. 计算机应用研究, 2025, 42 (6): 1880-1886. (Gao Mingyang, Geng Yan, Yu Xiao, et al. Automatic classification of leukemia subtypes based on multi-scale multi-feature hybrid model [J]. Application Research of Computers, 2025, 42 (6): 1880-1886. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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