Machine Learning (ML) Algorithms for Diagnosing Blood Cancer in Blood Smear Images

Amira Hassan Abed

doi:10.56427/jcbd.v4i2.756

Authors

Amira Hassan Abed information systems Department, AL Ryada Univercity for sciences & Technology

DOI:

https://doi.org/10.56427/jcbd.v4i2.756

Keywords:

Blood Cancer, Deep Learning, Medical Imaging, White Blood Cells, Classification

Abstract

Artificial intelligence (AI), particularly deep learning (DL), has significantly advanced medical image analysis, including the detection and classification of blood cancer through blood smear images. This review explores the state-of-the-art data mining (DM) and DL techniques applied in the identification and classification of white blood cells (WBCs), with a focus on leukemia diagnosis. By systematically analyzing relevant literature from 2014 to 2024, the study highlights key AI algorithms, including traditional machine learning models such as SVM, KNN, and ANN, as well as modern DL architectures like CNN, RCNN, ResNet, and hybrid models. The review evaluates their performance, clinical applicability, and implementation challenges. Particular attention is given to the strengths of DL in feature extraction and classification accuracy, which often surpass traditional DM approaches. Despite these advances, issues such as data scarcity, computational cost, and the need for medical expertise remain major challenges. The study also outlines future directions involving lightweight DL models, transfer learning, and open-access datasets to enhance clinical deployment. Ultimately, this work provides a comprehensive foundation for researchers and developers aiming to improve blood cancer diagnosis through automated medical imaging systems powered by AI.

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