ArticleDiagnostics (Basel, Switzerland)2021
An End-to-End Pipeline for Early Diagnosis of Acute Promyelocytic Leukemia Based on a Compact CNN Model.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 18 citations in OpenAlex.
- Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review).Oncology reports · 2026Review
- Improving acute lymphoblastic leukemia diagnosis through CBAM-enhanced VGG19 deep learning.Scientific reports · 2026Article
- A multi-scale feature fusion of deep learning network for classifying acute leukemia genetic subtypes from blood smear images.Discover oncology · 2026Article
- Article
- Applications of Artificial Intelligence in Acute Promyelocytic Leukemia: An Avenue of Opportunities? A Systematic Review.Journal of clinical medicine · 2025Review
- Hematopathological Patterns in Acute Myeloid Leukemia with Complications of Overt Disseminated Intravascular Coagulation.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence-assisted early screening of acute promyelocytic leukaemia in blood smears: a prospective evaluation of MC-100i.Frontiers in oncology · 2025Article
- Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency.Journal of pathology informatics · 2024Article
- Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images.Healthcare (Basel, Switzerland) · 2022Article
- A Deep Learning Approach for the Morphological Recognition of Reactive Lymphocytes in Patients with COVID-19 Infection.Bioengineering (Basel, Switzerland) · 2022Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Timely microscopy screening of peripheral blood smears is essential for the diagnosis of acute promyelocytic leukemia (APL) due to the occurrence of early death (ED) before or during the initial therapy. Screening manually is time-consuming and tedious, and may lead to missed diagnosis or misdiagnosis because of subjective bias. To address these problems, we develop a three-step pipeline to help in the early diagnosis of APL from peripheral blood smears. The entire pipeline consists of leukocytes focusing, cell classification and diagnostic opinions. As the key component of the pipeline, a compact classification model based on attention embedded convolutional neural network blocks is proposed to distinguish promyelocytes from normal leukocytes. The compact classification model is validated on both the combination of two public datasets, APL-Cytomorphology_LMU and APL-Cytomorphology_JHH, as well as the clinical dataset, to yield a precision of 96.53% and 99.20%, respectively. The results indicate that our model outperforms the other evaluated popular classification models owing to its better accuracy and smaller size. Furthermore, the entire pipeline is validated on realistic patient data. The proposed method promises to act as an assistant tool for APL diagnosis.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.