ArticleJournal of medical signals and sensors
Automatic Recognition of Acute Myelogenous Leukemia in Blood Microscopic Images Using K-means Clustering and Support Vector Machine.
Article in Journal of medical signals and sensors. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 83 citations in OpenAlex.
- Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis.Frontiers in big data · 2024Pooled it
- An Artificial Intelligence Framework for Detection of Leukemia with Comparison Between Segmented and Non-Segmented Images.Sensors (Basel, Switzerland) · 2026Article
- Efficient convolutional neural networks for acute lymphoblastic leukaemia prediction in computer vision.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
- Article
- Diagnosis of acute myeloid leukaemia on microarray gene expression data using categorical gradient boosted trees.Heliyon · 2023Article
- BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images.Sensors (Basel, Switzerland) · 2022Article
- Combined efficacy of Cinnamomum zeylanicum and doxorubicin against leukemia through regulation of TRAIL and NF-kappa B pathways in rat model.Molecular biology reports · 2022Article
- Artificial intelligence and its applications in digital hematopathology.Blood science (Baltimore, Md.) · 2022Article
- Leukemia can be Effectively Early Predicted in Routine Physical Examination with the Assistance of Machine Learning Models.Journal of healthcare engineering · 2022Article
- An End-to-End Pipeline for Early Diagnosis of Acute Promyelocytic Leukemia Based on a Compact CNN Model.Diagnostics (Basel, Switzerland) · 2021Article
- Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases.Clinical hematology international · 2021Review
- Detection and Classification of Immature Leukocytes for Diagnosis of Acute Myeloid Leukemia Using Random Forest Algorithm.Bioengineering (Basel, Switzerland) · 2020Article
- Cell Population Data-Driven Acute Promyelocytic Leukemia Flagging Through Artificial Neural Network Predictive Modeling.Translational oncology · 2020Article
- Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception.Frontiers in bioengineering and biotechnology · 2020Article
- Explainable Ensemble Learning With Stain Normalization and Deep Feature Extraction for Acute Lymphoblastic Leukaemia Classification.Healthcare technology lettersArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is characterized by the accumulation of myeloid blasts in the bone marrow. Careful microscopic examination of stained blood smear or bone marrow aspirate is still the most significant diagnostic methodology for initial AML screening and considered as the first step toward diagnosis. It is time-consuming and due to the elusive nature of the signs and symptoms of AML; wrong diagnosis may occur by pathologists. Therefore, the need for automation of leukemia detection has arisen. In this paper, an automatic technique for identification and detection of AML and its prevalent subtypes, i.e., M2-M5 is presented. At first, microscopic images are acquired from blood smears of patients with AML and normal cases. After applying image preprocessing, color segmentation strategy is applied for segmenting white blood cells from other blood components and then discriminative features, i.e., irregularity, nucleus-cytoplasm ratio, Hausdorff dimension, shape, color, and texture features are extracted from the entire nucleus in the whole images containing multiple nuclei. Images are classified to cancerous and noncancerous images by binary support vector machine (SVM) classifier with 10-fold cross validation technique. Classifier performance is evaluated by three parameters, i.e., sensitivity, specificity, and accuracy. Cancerous images are also classified into their prevalent subtypes by multi-SVM classifier. The results show that the proposed algorithm has achieved an acceptable performance for diagnosis of AML and its common subtypes. Therefore, it can be used as an assistant diagnostic tool for pathologists.
Indexed as
Identifiers
27563575PMC4973462W2498418174What OpenQuestion holds
Registered trials
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.