ArticleMedicine2020
AML, ALL, and CML classification and diagnosis based on bone marrow cell morphology combined with convolutional neural network: A STARD compliant diagnosis research.
Article in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- EfficientNet-B0-Based Screening of WBC-Diff Scattergram Images for Hematological Abnormalities.Cancer medicine · 2026Article
- Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review).Oncology reports · 2026Review
- Review
- Artificial Intelligence and Machine Learning in Pediatric Endocrine Tumors: Opportunities, Pitfalls, and a Roadmap for Trustworthy Clinical Translation.Biomedicines · 2026Review
- Artificial Intelligence in Hematologic Malignancies: Opportunities, Challenges, and Clinical Integration.Cureus · 2026Review
- Deep learning in bone marrow cytomorphology: advances in segmentation, classification, and clinical translation.Medical oncology (Northwood, London, England) · 2025Review
- Evaluation of the Circadian Rhythm Component Cipc (Clock-Interacting Pacemaker) in Leukemogenesis: A Literature Review and Bioinformatics Approach.Clocks & sleep · 2025Review
- Utilization of Machine Learning in the Prediction, Diagnosis, Prognosis, and Management of Chronic Myeloid Leukemia.International journal of molecular sciences · 2025Review
- Models for the marrow: A comprehensive review of AI-based cell classification methods and malignancy detection in bone marrow aspirate smears.HemaSphere · 2024Review
- Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review.BMC cancer · 2024Article
- A Chronological Overview of Using Deep Learning for Leukemia Detection: A Scoping Review.Cureus · 2024Article
- Y-box binding protein 1 in small extracellular vesicles reduces mesenchymal stem cell differentiation to osteoblasts-implications for acute myeloid leukaemia.Journal of extracellular vesicles · 2024Article
- Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?Cancers · 2024Review
- Recent advancements in machine learning for bone marrow cell morphology analysis.Frontiers in medicine · 2024Review
- Morphological diagnosis of hematologic malignancy using feature fusion-based deep convolutional neural network.Scientific reports · 2023Article
- High-accuracy morphological identification of bone marrow cells using deep learning-based Morphogo system.Scientific reports · 2023Article
- Deep learning enhances acute lymphoblastic leukemia diagnosis and classification using bone marrow images.Frontiers in oncology · 2023Review
- Artificial intelligence applications in pediatric oncology diagnosis.Exploration of targeted anti-tumor therapy · 2023Review
- Deep learning application of the discrimination of bone marrow aspiration cells in patients with myelodysplastic syndromes.Scientific reports · 2022Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Leukemia diagnosis based on bone marrow cell morphology primarily relies on the manual microscopy of bone marrow smears. However, this method is greatly affected by subjective factors and tends to lead to misdiagnosis. This study proposes using bone marrow cell microscopy images and employs convolutional neural network (CNN) combined with transfer learning to establish an objective, rapid, and accurate method for classification and diagnosis of LKA (AML, ALL, and CML). We collected cell microscopy images of 104 bone marrow smears (including 18 healthy subjects, 53 AML patients, 23 ALL patients, and 18 CML patients). The perfect reflection algorithm and a self-adaptive filter algorithm were first used for preprocessing of bone marrow cell images collected from experiments. Subsequently, 3 CNN frameworks (Inception-V3, ResNet50, and DenseNet121) were used to construct classification models for the raw dataset and preprocessed dataset. Transfer learning was used to improve the prediction accuracy of the model. Results showed that the DenseNet121 model based on the preprocessed dataset provided the best classification results, with a prediction accuracy of 74.8%. The prediction accuracy of the DenseNet121 model that was obtained by transfer learning optimization was 95.3%, which was increased by 20.5%. In this model, the prediction accuracies of the normal groups, AML, ALL, and CML were 90%, 99%, 97%, and 95%, respectively. The results showed that the leukemic cell morphology classification and diagnosis based on CNN combined with transfer learning is feasible. Compared with conventional manual microscopy, this method is more rapid, accurate, and objective.
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