Pattern Recognition in Blast Cells by Improved Image Processing Techniques

Year 2025, Volume: 12 Issue: 4, 935 - 952, 31.12.2025

Fatma Akalın , Mehmet Fatih Orhan

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

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

Acute Lymphoblastic Leukemia , Blast Detection , Improved Image Processing Techniques , Transfer Learning Architecture , Ensemble Learning Approach

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