Pattern Recognition in Blast Cells by Improved Image Processing Techniques

Fatma Akalın; Mehmet Fatih Orhan

doi:10.54287/gujsa.1735961

Pattern Recognition in Blast Cells by Improved Image Processing Techniques

Abstract

Acute lymphoblastic leukemia (ALL) is a critical type of cancer affecting white blood cell development. It is characterized by the disruption of normal stem cell maturation processes or the excessive proliferation of leukemic cells. As the most common childhood cancer, ALL is a significant health problem requiring continuous clinical monitoring. A major challenge in diagnosing the disease stems from the non-specific nature of the initial symptoms. Common symptoms, such as fever, fatigue, headache, weight loss, and musculoskeletal disorders, can mimic many diseases, making diagnostic accuracy challenging. This ambiguity leads to delayed diagnosis and negatively impacts treatment success. Although the incidence of leukemia has increased in recent years, advances in medical technology have shown promising results in reducing mortality rates. Health information systems play a significant role in this success by facilitating early disease diagnosis. In our research, two different image enhancement methods were developed to highlight the characteristics of blast cells, a critical indicator for the diagnosis of ALL. Three image groups were evaluated: unprocessed original images and two different processed image sets (versions 1 and 2). Classification performed using the MobileNetv2 transfer learning framework achieved accuracy rates of 85%, 90%, and 89% on the test dataset, respectively. To enhance diagnostic reliability beyond single-model performance, an ensemble architecture combining eight different transfer learning networks was created. This optimized ensemble model, using images enhanced with the high-performing version 1 filter, achieved approximately 90% classification accuracy. This result represents a significant advancement in AI-assisted hematological image interpretation.

Keywords

References

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Details

Primary Language

English

Subjects

Biomedical Diagnosis

Journal Section

Research Article

Authors

Fatma Akalın ^*
0000-0001-6670-915X
Türkiye

Mehmet Fatih Orhan
0000-0001-8081-6760
Türkiye

Publication Date

December 31, 2025

Submission Date

July 6, 2025

Acceptance Date

November 24, 2025

Published in Issue

Year 2025 Volume: 12 Number: 4

DOI

https://doi.org/10.54287/gujsa.1735961

IZ

https://izlik.org/JA35EF28SF

Cite

RIS / Bibtex

APA

Akalın, F., & Orhan, M. F. (2025). Pattern Recognition in Blast Cells by Improved Image Processing Techniques. Gazi University Journal of Science Part A: Engineering and Innovation, 12(4), 935-952. https://doi.org/10.54287/gujsa.1735961

AMA

1.Akalın F, Orhan MF. Pattern Recognition in Blast Cells by Improved Image Processing Techniques. GU J Sci, Part A. 2025;12(4):935-952. doi:10.54287/gujsa.1735961

Chicago

Akalın, Fatma, and Mehmet Fatih Orhan. 2025. “Pattern Recognition in Blast Cells by Improved Image Processing Techniques”. Gazi University Journal of Science Part A: Engineering and Innovation 12 (4): 935-52. https://doi.org/10.54287/gujsa.1735961.

EndNote

Akalın F, Orhan MF (December 1, 2025) Pattern Recognition in Blast Cells by Improved Image Processing Techniques. Gazi University Journal of Science Part A: Engineering and Innovation 12 4 935–952.

IEEE

[1]F. Akalın and M. F. Orhan, “Pattern Recognition in Blast Cells by Improved Image Processing Techniques”, GU J Sci, Part A, vol. 12, no. 4, pp. 935–952, Dec. 2025, doi: 10.54287/gujsa.1735961.

ISNAD

Akalın, Fatma - Orhan, Mehmet Fatih. “Pattern Recognition in Blast Cells by Improved Image Processing Techniques”. Gazi University Journal of Science Part A: Engineering and Innovation 12/4 (December 1, 2025): 935-952. https://doi.org/10.54287/gujsa.1735961.

JAMA

1.Akalın F, Orhan MF. Pattern Recognition in Blast Cells by Improved Image Processing Techniques. GU J Sci, Part A. 2025;12:935–952.

MLA

Akalın, Fatma, and Mehmet Fatih Orhan. “Pattern Recognition in Blast Cells by Improved Image Processing Techniques”. Gazi University Journal of Science Part A: Engineering and Innovation, vol. 12, no. 4, Dec. 2025, pp. 935-52, doi:10.54287/gujsa.1735961.

Vancouver

1.Fatma Akalın, Mehmet Fatih Orhan. Pattern Recognition in Blast Cells by Improved Image Processing Techniques. GU J Sci, Part A. 2025 Dec. 1;12(4):935-52. doi:10.54287/gujsa.1735961