InceptionV3-Based Blood Cell Classification for Cancer Detection

Runhai He; Quanhua Zhou

doi:10.70393/6a6374616d.323737

OPEN ACCESS |Research Article ||1 March 2025

InceptionV3-Based Blood Cell Classification for Cancer Detection

Runhai He

Belarusian State University

fpm.he@bsu.by

Faculty of Applied Mathematics and Computer Science, Belarusian State University, Minsk, Belarus.

Quanhua Zhou

Belarusian State University

2926634184@qq.com

Faculty of Applied Mathematics and Computer Science, Belarusian State University, Minsk, Belarus.

* Corresponding Author¹: Runhai He, E-Mail: fpm.he@bsu.by

Publication

Accepted 2024 December 2 ; Published 2025 March 1

Journal of Computer Technology and Applied Mathematics, 2025, 2(2), 3007-4126.

Abstract

Blood cell morphological analysis plays a vital role in clinical diagnosis, especially in the early detection of leukemia, anemia and other blood system diseases. Conventional image processing techniques are difficult to deal with complex situations such as cell overlap and uneven staining, and basic machine learning methods also have obvious limitations in extracting complex morphological features. Deep learning has shown excellent performance in the field of medical image classification and provides a new technical approach for automated analysis of blood cells. This study aims to develop an efficient and accurate blood cell classification model to assist in the early diagnosis of blood diseases and cancer. By adopting the InceptionV3 network structure and combining the 'Grid Search Enhanced with Coordinate Ascent' hyperparameter optimization method, the study provides a systematic automated classification model training method for blood cell multi-classification tasks. The experiment was based on a dataset containing five types of cells. The results showed that the final model achieved an accuracy of 99.20% on the test set, the AUC of all classes reached 1.00, and the average specificity was as high as 99.80%, providing a reliable technical reference for clinical blood pathology analysis and early cancer screening.

Keywords

Inception , Blood Cell , Image Classification , Deep Learning , Hyperparameters Optimization .

Metadata

DOI:

10.70393/6a6374616d.323737

ARK:

ark:/40704/JCTAM.v2n2a04

Pages: 24-30

References: 13

Disciplines: Computer Science

Subjects: Image Classification

Cite This Article

APA Style

He, R. & Zhou, Q. (2025). Inceptionv3-based blood cell classification for cancer detection. Journal of Computer Technology and Applied Mathematics, 2(2), 24-30. https://doi.org/10.70393/6a6374616d.323737

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
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