ArticleBioengineering (Basel, Switzerland)2020
Detection and Classification of Immature Leukocytes for Diagnosis of Acute Myeloid Leukemia Using Random Forest Algorithm.
Article in Bioengineering (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- XHIC-Net: An Explainable Hybrid Involution-Convolution Network for Blood Smear Cell Morphology Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Aligning deep learning and interpretable models for blood cell classification: A dual-model framework for explainability.PLOS digital health · 2026Article
- Hybrid handcrafted and deep feature fusion for automated acute myeloid leukemia classification using TCMA-Net on a class-balanced dataset.Frontiers in oncology · 2026Article
- Artificial Intelligence in Hematologic Malignancies: Opportunities, Challenges, and Clinical Integration.Cureus · 2026Review
- AI-Based Computational Model Integrating Routinely Acquired Blood Parameters for Triage and Early Detection of Acute Myeloid Leukemia.Biomedical engineering and computational biology · 2026Article
- Dynamic kernel generation through hybrid involution and convolution neural networks for leukemia and white blood cell classification.Scientific reports · 2025Article
- An improved multiclass classification of acute lymphocytic leukemia using enhanced glowworm swarm optimization.Scientific reports · 2025Article
- Research advances in the adjunctive diagnosis of acute myeloid leukemia.Frontiers in oncology · 2025Review
- Digital infrared thermography and machine learning for diabetic foot assessment: thermal patterns and classification.Journal of diabetes and metabolic disorders · 2024Article
- Article
- High-accuracy morphological identification of bone marrow cells using deep learning-based Morphogo system.Scientific reports · 2023Article
- 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
- Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features.Sensors (Basel, Switzerland) · 2022Article
- A Fully Unsupervised Deep Learning Framework for Non-Rigid Fundus Image Registration.Bioengineering (Basel, Switzerland) · 2022Article
- A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects.Journal of medical Internet research · 2022Review
- Biomedical Diagnosis of Leukemia Using a Deep Learner Classifier.Computational intelligence and neuroscience · 2022Article
- Morphological, fractal, and textural features for the blood cell classification: the case of acute myeloid leukemia.European biophysics journal : EBJ · 2021Article
- Pop Music Trend and Image Analysis Based on Big Data Technology.Computational intelligence and neuroscience · 2021Article
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3 authors.
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Abstract
Acute myeloid leukemia (AML) is a fatal blood cancer that progresses rapidly and hinders the function of blood cells and the immune system. The current AML diagnostic method, a manual examination of the peripheral blood smear, is time consuming, labor intensive, and suffers from considerable inter-observer variation. Herein, a machine learning model to detect and classify immature leukocytes for efficient diagnosis of AML is presented. Images of leukocytes in AML patients and healthy controls were obtained from a publicly available dataset in The Cancer Imaging Archive. Image format conversion, multi-Otsu thresholding, and morphological operations were used for segmentation of the nucleus and cytoplasm. From each image, 16 features were extracted, two of which are new nucleus color features proposed in this study. A random forest algorithm was trained for the detection and classification of immature leukocytes. The model achieved 92.99% accuracy for detection and 93.45% accuracy for classification of immature leukocytes into four types. Precision values for each class were above 65%, which is an improvement on the current state of art. Based on Gini importance, the nucleus to cytoplasm area ratio was a discriminative feature for both detection and classification, while the two proposed features were shown to be significant for classification. The proposed model can be used as a support tool for the diagnosis of AML, and the features calculated to be most important serve as a baseline for future research.
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