ArticleFrontiers in bioengineering and biotechnology2020
Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in Leukemia detection and classification: A Systematic review of AI and image processing techniques.F1000Research · 2024Pooled it
- Deep Learning Neural Network Based on PSO for Leukemia Cell Disease Diagnosis from Microscope Images.Journal of imaging informatics in medicine · 2025Article
- An improved multiclass classification of acute lymphocytic leukemia using enhanced glowworm swarm optimization.Scientific reports · 2025Article
- Blood cancer prediction model based on deep learning technique.Scientific reports · 2025Article
- An explainable deep learning-based feature fusion model for acute lymphoblastic leukemia diagnosis and severity assessment.Frontiers in medicine · 2025Article
- ALL-Net: integrating CNN and explainable-AI for enhanced diagnosis and interpretation of acute lymphoblastic leukemia.PeerJ. Computer science · 2025Article
- Advancements in Hematologic Malignancy Detection: A Comprehensive Survey of Methodologies and Emerging Trends.TheScientificWorldJournal · 2025Review
- A reliable approach for identifying acute lymphoblastic leukemia in microscopic imaging.Frontiers in artificial intelligence · 2025Article
- Application of image recognition technology in pathological diagnosis of blood smears.Clinical and experimental medicine · 2024Review
- ALL classification using neural ensemble and memetic deep feature optimization.Frontiers in artificial intelligence · 2024Article
- An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.Frontiers in oncology · 2024Article
- Hybrid Feature-Learning-Based PSO-PCA Feature Engineering Approach for Blood Cancer Classification.Diagnostics (Basel, Switzerland) · 2023Article
- A high-resolution large-scale dataset of pathological and normal white blood cells.Scientific data · 2023Article
- ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images.Diagnostics (Basel, Switzerland) · 2023Article
- BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images.Sensors (Basel, Switzerland) · 2022Article
- A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects.Journal of medical Internet research · 2022Review
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Microscopic image analysis plays a significant role in initial leukemia screening and its efficient diagnostics. Since the present conventional methodologies partly rely on manual examination, which is time consuming and depends greatly on the experience of domain experts, automated leukemia detection opens up new possibilities to minimize human intervention and provide more accurate clinical information. This paper proposes a novel approach based on conventional digital image processing techniques and machine learning algorithms to automatically identify acute lymphoblastic leukemia from peripheral blood smear images. To overcome the greatest challenges in the segmentation phase, we implemented extensive pre-processing and introduced a three-phase filtration algorithm to achieve the best segmentation results. Moreover, sixteen robust features were extracted from the images in the way that hematological experts do, which significantly increased the capability of the classifiers to recognize leukemic cells in microscopic images. To perform the classification, we applied two traditional machine learning classifiers, the artificial neural network and the support vector machine. Both methods reached a specificity of 95.31%, and the sensitivity of the support vector machine and artificial neural network reached 98.25 and 100%, respectively.
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