https://doi.org/10.1140/epjs/s11734-024-01301-4
Regular Article
Acute lymphoblastic leukemia classification using persistent homology
1
Departamento de Ciencias Exactas y Tecnología, Centro Universitario de los Lagos, Universidad de Guadalajara, Enrique Díaz de León Colonia Paseos de la Montaña, 1144, Lagos de Moreno, Jalisco, Mexico
2
SZABIST University, 44000, Islamabad, Pakistan
3
COMSATS University Islamabad, Park road, 44000, Islamabad, Pakistan
4
International Islamic University, Islamabad, Pakistan
5
Center for Biomedical Technology, Universidad Politécnica de Madrid, Campus Montegancedo, Pozuelo de Alarcón, 28223, Madrid, Spain
d
sohail_iqbal@comsats.edu.pk
f
alexander.pisarchik@upm.es
Received:
13
June
2024
Accepted:
2
August
2024
Published online:
6
September
2024
Acute Lymphoblastic Leukemia (ALL) is a prevalent form of childhood blood cancer characterized by the proliferation of immature white blood cells that rapidly replace normal cells in the bone marrow. The exponential growth of these leukemic cells can be fatal if not treated promptly. Classifying lymphoblasts and healthy cells poses a significant challenge, even for domain experts, due to their morphological similarities. Automated computer analysis of ALL can provide substantial support in this domain and potentially save numerous lives. In this paper, we propose a novel classification approach that involves analyzing shapes and extracting topological features of ALL cells. We employ persistent homology to capture these topological features. Our technique accurately and efficiently detects and classifies leukemia blast cells, achieving a recall of 98.2% and an F1-score of 94.6%. This approach has the potential to significantly enhance leukemia diagnosis and therapy.
© The Author(s) 2024
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