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Deep learning-based detection and classification of acute lymphoblastic leukemia with explainable AI techniques

https://doi.org/10.1016/j.array.2025.100397

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1 of the 51 references listed on this page is in the Retraction Watch database as retracted (export 2026-11-11): 10.1007/s00500-022-07420-1 (notice 2023-08-10).

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What this work says

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ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification

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CapsENet: Deep Learning based Acute Lymphoblastic Leukemia Detection Approach

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Acute Lymphoblastic Leukemia Detection Based on Adaptive Unsharpening and Deep Learning

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Analyzing Acute Lymphoblastic Leukemia Across Multiple Classes Using an Enhanced Deep Convolutional Neural Network on Blood Smear

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Texture-based Feature Extraction and Machine Learning Model for the Detection of Acute Lymphoblastic Leukemia

inferredcites →https://doi.org/10.1109/icoiact.2018.8350822openalex-snapshot

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DeepLymphoDetect: Leveraging Deep Learning Techniques for Acute Lymphoblastic Leukemia Detection in Blood Cells

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Acute leukemia classification by using SVM and K-Means clustering

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Multi-class Investigation of Acute Lymphoblastic Leukemia using Optimized Deep Convolutional Neural Network on Blood Smear Images

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Hybrid Inception v3 XGBoost Model for Acute Lymphoblastic Leukemia Classification

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Method for Diagnosis of Acute Lymphoblastic Leukemia Based on ViT‐CNN Ensemble Model

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Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review

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Automated Detection Model in Classification of B-Lymphoblast Cells from Normal B-Lymphoid Precursors in Blood Smear Microscopic Images Based on the Majority Voting Technique

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Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks

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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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Survey on automated leukemia detection: bridging medical imaging and artificial intelligence

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Morpho-spectral YOLOv10-HSNet: lightweight hybrid model for multi-class leukocyte detection via attention-guided signal analysis

inferred← citeshttps://doi.org/10.1007/s11831-026-10632-5openalex-snapshot

Recent Advances in Deep Learning for Automated Acute Lymphoblastic Leukemia Detection from Blood Smear Images: A Systematic Survey

inferred← citeshttps://doi.org/10.1007/s42452-026-09129-4openalex-snapshot

YOLOv8-based detection, subtyping, and cell counting of B-cell acute lymphoblastic leukemia in peripheral blood smear images

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inferred← citeshttps://doi.org/10.1109/iccit68739.2025.11491624openalex-snapshot

TM-DeepNet: A Hybrid Deep Learning Model with Explainable AI for Acute Lymphoblastic Leukemia Stage Classification

inferred← citeshttps://doi.org/10.1109/iccit68739.2025.11491680openalex-snapshot

Multi-Task Deep Learning Framework for Leukemia Cell Classification and Segmentation: A Joint Learning Approach

inferred← citeshttps://doi.org/10.1109/iccrtee68719.2026.11566213openalex-snapshot

Optimized Deep Learning Framework for Blood Cancer Diagnosis

inferred← citeshttps://doi.org/10.1109/icemcsi67638.2026.11602860openalex-snapshot

A Survey of Deep Learning Methods for Leukemia Detection and Classification

inferred← citeshttps://doi.org/10.1109/icitsif69060.2026.11608961openalex-snapshot

Hybrid Deep Learning Ensemble Framework for Early Detection and Classification of Blood Cancer

inferred← citeshttps://doi.org/10.1109/icoecit68303.2026.11496984openalex-snapshot

White Blood Cell Recognition Through Transfer Learning and Deep Fine-Tuning

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inferred← citeshttps://doi.org/10.1109/ipta66025.2025.11222027openalex-snapshot

Robustness of Deep Learning-Based for Acute Lymphoblastic Leukemia Detection and Classification

inferred← citeshttps://doi.org/10.1109/qpain69676.2026.11546053openalex-snapshot

Acute Lymphoblastic Leukemia Detection Using Deep Learning Architecture and Advanced Feature Engineering Techniques

inferred← citeshttps://doi.org/10.1109/tqcebt67648.2026.11681491openalex-snapshot

A Hybrid Quantum-Classical Framework for Acute Lymphoblastic Leukemia Detection Using TSP-Based Nuclear Ordering and Parametrized Quantum Circuits

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Multiscale contextual attention network for robust diagnosis of acute lymphoblastic leukemia in blood smears: Implications for clinical adoption and the role of medical science liaisons

inferred← citeshttps://doi.org/10.21203/rs.3.rs-10083317/v1openalex-snapshot

Graph-Aware Medical Image Intelligence: A Comparative Study of CNNs, GNNs, and Multimodal LLMs for Acute Lymphoblastic Leukaemia Subtyping

inferred← citeshttps://doi.org/10.26483/ijarcs.v16i3.7293openalex-snapshot

RECENT DEVELOPMENTS IN THE DETECTION AND CLASSIFICATION OF LEUKEMIA - A COMPREHENSIVE EVALUATION OF DEEP LEARNING SOLUTIONS

inferred← citeshttps://doi.org/10.3389/fcell.2025.1674393openalex-snapshot

CausalFormer-HMC: a hybrid memory-driven transformer with causal reasoning and counterfactual explainability for leukemia diagnosis

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