ArticleEuropean biophysics journal : EBJ2021
Morphological, fractal, and textural features for the blood cell classification: the case of acute myeloid leukemia.
Article in European biophysics journal : EBJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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8 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Research advances in the adjunctive diagnosis of acute myeloid leukemia.Frontiers in oncology · 2025Review
- Fractal analysis of peripheral blood neutrophil chromatin in patients infected with Trypanosoma cruzi and/or HIV correlates with left ventricle ejection fraction, T CD4+, and T CD8+ lymphocytes levels.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2025Article
- Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency.Journal of pathology informatics · 2024Article
- Application of artificial neural network for the mechano-bactericidal effect of bioinspired nanopatterned surfaces.European biophysics journal : EBJ · 2024Article
- Article
- Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2023Article
- Human umbilical cord blood cells suffer major modification by fixatives and anticoagulants.Frontiers in physiology · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microscopic examination of stained peripheral blood smears is, nowadays, an indispensable tool in the evaluation of patients with hematological and non-hematological diseases. While a rapid automated quantification of the regular blood cells is available, recognition and counting of immature white blood cells (WBC) still relies mostly on the microscopic examination of blood smears by an experienced observer. Recently, there are efforts to improve the prediction by various machine learning approaches. An open dataset collection including the recently digitalized single-cell images for 200 patients, from peripheral blood smears at 100 × magnification, was used. We studied different morphological, fractal, and textural descriptors for WBC classification, with an aim to indicate the most reliable parameters for the recognition of certain cell types. Structural properties of both the mature and non-mature leukocytes obtained from (i) acute myeloid leukemia patients, or (ii) non-malignant controls, were studied in depth, with a sample size of about 25 WBC per group. We quantified structural and textural differences and, based on the statistical ranges of parameters for different WBC types, selected eight features for classification: Cell area, Nucleus-to-cell ratio, Nucleus solidity, Fractal dimension, Correlation, Contrast, Homogeneity, and Energy. Classification Precision of up to 100% (80% on average) was achieved.
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