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A Customized Efficient Deep Learning Model for the Diagnosis of Acute Leukemia Cells Based on Lymphocyte and Monocyte Images
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s42979-020-0114-9openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.artmed.2014.09.002openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.clml.2021.06.025openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.cmpb.2019.104987openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.compbiomed.2022.105236openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.engappai.2018.04.024openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.measurement.2014.04.008openalex-snapshot
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/jemt.70156openalex-snapshot
Deep Learning Based Framework for Detection and Classification of Leukemia Using Microscopic Images
inferred← citeshttps://doi.org/10.1007/978-3-031-49002-6_13openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-031-71481-8_1openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-032-19130-4_26openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11042-023-15923-8openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11831-025-10401-wopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11831-026-10512-yopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s13198-025-02843-zopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s41870-024-02126-zopenalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.array.2023.100292openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.array.2025.100397openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.artmed.2024.102883openalex-snapshot
Hematologic cancer diagnosis and classification using machine and deep learning: State-of-the-art techniques and emerging research directives
inferred← citeshttps://doi.org/10.1016/j.artmed.2026.103393openalex-snapshot
A systematic review of machine and deep learning techniques for acute lymphoblastic leukemia diagnosis
inferred← citeshttps://doi.org/10.1016/j.blre.2026.101398openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106722openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.bspc.2025.107840openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compeleceng.2024.109446openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.fri.2025.200670openalex-snapshot
A five-year systematic review of AI-based medical image analysis: From preprocessing to classification
inferred← citeshttps://doi.org/10.1016/j.imu.2024.101542openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.retram.2026.103598openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41524-023-01037-0openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-023-44210-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-025-28040-3openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-026-40184-4openalex-snapshot
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inferred← citeshttps://doi.org/10.1049/tje2.70228openalex-snapshot
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inferred← citeshttps://doi.org/10.1063/5.0234340openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/03772063.2026.2637624openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/access.2023.3260065openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/access.2023.3266511openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/access.2024.3368031openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/access.2025.3563256openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/access.2025.3573277openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/aic57670.2023.10263933openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/becithcon64160.2024.10962577openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/fit67061.2025.11333717openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icc-robins60238.2024.10534018openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/iccece58645.2024.10497341openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/iciccs67901.2026.11502625openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/iciics67880.2026.11483370openalex-snapshot
Comprehensive Analysis of Computational-Based Analytical Approaches for Acute Lymphoblastic Leukemia Detection and Classification
inferred← citeshttps://doi.org/10.1109/icimia67127.2025.11200635openalex-snapshot
Dynamic Edge-Modulated Dual-Branch Network for Leukemia Cell Segmentation
inferred← citeshttps://doi.org/10.1109/icrteect67512.2025.11448901openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/ictest60614.2024.10576117openalex-snapshot
A Review on Automated Detection of Acute Myeloid Leukemia
inferred← citeshttps://doi.org/10.1111/jebm.70005openalex-snapshot
Towards Diagnostic Intelligent Systems in Leukemia Detection and Classification: A Systematic Review and Meta‐analysis
inferred← citeshttps://doi.org/10.1177/18758967261460501openalex-snapshot
A Dataset-Centric Analytical Review of Automated Leukaemia Detection: Performance Trends, Methodological Variability, and Research Gaps
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3880624/v1openalex-snapshot
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inferred← citeshttps://doi.org/10.21203/rs.3.rs-3880624/v2openalex-snapshot
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inferred← citeshttps://doi.org/10.22399/ijcesen.5285openalex-snapshot
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inferred← citeshttps://doi.org/10.30574/wjarr.2024.24.3.3971openalex-snapshot
Privacy-preserving multi-class classification of acute lymphoblastic leukemia subtypes using federated learning
inferred← citeshttps://doi.org/10.31649/1999-9941-2023-58-3-84-93openalex-snapshot
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inferred← citeshttps://doi.org/10.3233/thc-240750openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/applbiosci4010009openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/bioengineering13080938openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/diagnostics13061026openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/diagnostics13132258openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/diagnostics13162672openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/diagnostics13172752openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/sym17010024openalex-snapshot
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inferred← citeshttps://doi.org/10.35882/jeeemi.v6i1.354openalex-snapshot
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inferred← citeshttps://doi.org/10.4018/979-8-3693-5261-8.ch007openalex-snapshot
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inferred← citeshttps://doi.org/10.4236/jcc.2026.143014openalex-snapshot
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inferred← citeshttps://doi.org/10.48084/etasr.12790openalex-snapshot
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inferred← citeshttps://doi.org/10.7717/peerj-cs.3335openalex-snapshot
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inferred← citeshttps://doi.org/10.7717/peerj-cs.3570openalex-snapshot
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