Intelligent Data Processing and Decision Systems for Leukemia Identification via Machine Learning
Year: 2026
Type: article
Abstract: Conventional leukemia diagnosis depends on labour-intensive manual analysis of blood samples and bone marrow aspirations, which often proves slow, inconsistent, and error-prone. This study evaluates a machine learning-based approach for automated classification of leukemia - related microscopic blood-smear images. The study focuses on machine learning and deep learning methods for classifying microscopic blood smear images, with experimental evaluation conducted on the C-NMC 2019 dataset. The proposed experimental pipeline consists of image preprocessing, model-based feature representation, weighted ensemble classification, and test-time augmentation followed by evaluation using accuracy, precision, F1-score, and confusion-matrix analysis. A weighted ensemble approach with Test-Time Augmentation (TTA) was evaluated on a held-out test set to distinguish Acute Lymphoblastic Leukemia (ALL) from hematogones (HEM). The evaluated model achieved an overall accuracy of 93.51%, with a macro precision of 0.9201, macro recall of 0.9347, and macro F1-score of 0.9268. On the held-out Fold 0 test set of 3,527 images, 2,243 ALL images and 1,055 HEM images were correctly classified. The findings demonstrate the potential of machine-learning-based microscopic image analysis as a decision-support approach; however, the results are experimental and require validation on independent clinical datasets before clinical deployment.
Authors: Kiran, Anil Sangwan
Institution: Maharshi Dayanand University
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7222408809
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