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A Deep Learning-Based Approach for the Diagnosis of Acute Lymphoblastic Leukemia
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2 of the 38 references listed on this page are in the Retraction Watch database as retracted (export 2026-11-11): 10.1155/2021/9500304 (notice 2023-08-30), 10.1155/2022/6486570 (notice 2023-11-29).
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Classification of acute lymphoblastic leukemia using deep learning
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An efficient deep Convolutional Neural Network based detection and classification of Acute Lymphoblastic Leukemia
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Ensemble of Convolutional Neural Networks to diagnose Acute Lymphoblastic Leukemia from microscopic images
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Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features
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Privileged Modality Distillation for Vessel Border Detection in Intracoronary Imaging
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Image transformers for classifying acute lymphoblastic leukemia
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Method for Diagnosis of Acute Lymphoblastic Leukemia Based on ViT‐CNN Ensemble Model
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Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks
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Classifying microscopic images as acute lymphoblastic leukemia by Resnet ensemble model and Taguchi method
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Automatic Early Detection and Classification of Leukemia from Microscopic Blood Image
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An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Statistical performance review on diagnosis of leukemia, glaucoma and diabetes mellitus using AI
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Fusing ResNet50 and VGG16 for Enhanced Diagnosis of Acute Lymphoblastic Leukemia: A MultiNet Ensemble Approach
inferred← citeshttps://doi.org/10.1007/978-3-032-13803-3_28openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s12672-025-03985-zopenalex-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/htl2.70091openalex-snapshot
Explainable Ensemble Learning With Stain Normalization and Deep Feature Extraction for Acute Lymphoblastic Leukaemia Classification
inferred← citeshttps://doi.org/10.1063/5.0234340openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/03772063.2026.2687636openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/10428194.2025.2583449openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/aiei69164.2026.11497353openalex-snapshot
SGALL: SinGAN and YOLOv8 generated Acute Lymphoblastic Leukemia dataset for enhanced Detection and Classification
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Automated Blast Cell Detection for Acute Lymphoblastic Leukemia Using a Stacking Ensemble of Convolutional Neural Networks
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Multi-Task Deep Learning Framework for Leukemia Cell Classification and Segmentation: A Joint Learning Approach
inferred← citeshttps://doi.org/10.1109/icdici62993.2024.10810888openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/iceconf57129.2023.10084025openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icidca66325.2025.11280469openalex-snapshot
Certain Investigation on Deep Learning Algorithms for Leukemia Classification
inferred← citeshttps://doi.org/10.1109/iciics67880.2026.11483370openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icoici62503.2024.10695974openalex-snapshot
Convolutional Neural Network-Based Approach for Classification of Blood Cells in Leukaemia Detection
inferred← citeshttps://doi.org/10.1109/icosaas68663.2026.11649011openalex-snapshot
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Vision Transformer (ViT)-based Approach for Accurate Detection of Acute Lymphoblastic Leukemia
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Effective Hybrid Pipeline for Acute Lymphoblastic Leukemia Detection Using FCM Segmentation, Efficientnet Features, and XGBoost Classification
inferred← citeshttps://doi.org/10.1111/jebm.70005openalex-snapshot
Towards Diagnostic Intelligent Systems in Leukemia Detection and Classification: A Systematic Review and Meta‐analysis
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A framework for leukemia detection based on multi fusion strategy
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