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Hybrid adversarial‐discriminative network for leukocyte classification in leukemia

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/cyto.a.22457openalex-snapshot

A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images

inferredcites →https://doi.org/10.1002/mp.12381openalex-snapshot

Computerized detection of leukocytes in microscopic leukorrhea images

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A deep 3D residual CNN for false‐positive reduction in pulmonary nodule detection

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Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion

inferredcites →https://doi.org/10.1002/mp.13510openalex-snapshot

Attention‐aware fully convolutional neural network with convolutional long short‐term memory network for ultrasound‐based motion tracking

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Accurate colorectal tumor segmentation for CT scans based on the label assignment generative adversarial network

inferredcites →https://doi.org/10.1007/978-3-030-01234-2_18openalex-snapshot

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

inferredcites →https://doi.org/10.1007/978-3-319-63754-9_7openalex-snapshot

Computer-Aided Acute Lymphoblastic Leukemia Diagnosis System Based on Image Analysis

inferredcites →https://doi.org/10.1007/978-3-642-13775-4_17openalex-snapshot

Automatic Recognition of Five Types of White Blood Cells in Peripheral Blood

inferredcites →https://doi.org/10.1007/s11042-017-4478-3openalex-snapshot

Classification of acute lymphoblastic leukaemia using hybrid hierarchical classifiers

inferredcites →https://doi.org/10.1007/s12021-018-9377-xopenalex-snapshot

SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation

inferredcites →https://doi.org/10.1016/j.cmpb.2018.05.024openalex-snapshot

Fine-grained leukocyte classification with deep residual learning for microscopic images

inferredcites →https://doi.org/10.1016/j.ijleo.2016.09.046openalex-snapshot

Automatic detection of leukocytes for cytometry with color decomposition

inferredcites →https://doi.org/10.1016/j.measurement.2014.04.008openalex-snapshot

Automatic segmentation, counting, size determination and classification of white blood cells

inferredcites →https://doi.org/10.1016/j.measurement.2017.11.002openalex-snapshot

White blood cell (WBC) counting analysis in blood smear images using various color segmentation methods

inferredcites →https://doi.org/10.1016/j.media.2018.07.010openalex-snapshot

Building medical image classifiers with very limited data using segmentation networks

inferredcites →https://doi.org/10.1063/1.5002036openalex-snapshot

White blood cell counting analysis of blood smear images using various segmentation strategies

inferredcites →https://doi.org/10.1109/access.2018.2846685openalex-snapshot

Combining Convolutional Neural Network With Recursive Neural Network for Blood Cell Image Classification

inferredcites →https://doi.org/10.1109/cvpr.1993.341010openalex-snapshot

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot

Fully convolutional networks for semantic segmentation

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot

Densely Connected Convolutional Networks

inferredcites →https://doi.org/10.1109/cvpr.2017.476openalex-snapshot

Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.683openalex-snapshot

Residual Attention Network for Image Classification

inferredcites →https://doi.org/10.1109/cvpr.2018.00199openalex-snapshot

Learning a Discriminative Feature Network for Semantic Segmentation

inferredcites →https://doi.org/10.1109/cvprw.2014.131openalex-snapshot

CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

inferredcites →https://doi.org/10.1109/iccv.2015.178openalex-snapshot

Learning Deconvolution Network for Semantic Segmentation

inferredcites →https://doi.org/10.1109/icece.2016.7853957openalex-snapshot

A hybrid deep model with HOG features for Bangla handwritten numeral classification

inferredcites →https://doi.org/10.1109/icsmb.2010.5735344openalex-snapshot

Automated cell nucleus segmentation and acute leukemia detection in blood microscopic images

inferredcites →https://doi.org/10.1109/naecon.2018.8556686openalex-snapshot

Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net)

inferredcites →https://doi.org/10.1186/s12938-015-0037-1openalex-snapshot

Segmentation of white blood cells and comparison of cell morphology by linear and naïve Bayes classifiers

inferredcites →https://doi.org/10.20532/cit.2018.1004123openalex-snapshot

A Framework for Efficient Recognition and Classification of Acute Lymphoblastic Leukemia with a Novel Customized-KNN Classifier

inferredcites →https://doi.org/10.4103/2153-3539.109883openalex-snapshot

Scalable system for classification of white blood cells from Leishman stained blood stain images

inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot

Very Deep Convolutional Networks for Large-Scale Image Recognition

inferredcites →https://doi.org/10.48550/arxiv.1605.08695openalex-snapshot

TensorFlow: A system for large-scale machine learning

inferredcites →https://doi.org/10.57702/o9raffedopenalex-snapshot

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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An XAI Integrated Identification System of White Blood Cell Type Using Variants of Vision Transformer

inferred← citeshttps://doi.org/10.1007/s10462-021-10074-4openalex-snapshot

Cancer diagnosis using artificial intelligence: a review

inferred← citeshttps://doi.org/10.1007/s11042-023-16520-5openalex-snapshot

Cancer detection and segmentation using machine learning and deep learning techniques: a review

inferred← citeshttps://doi.org/10.1007/s11042-024-18373-yopenalex-snapshot

Automated segmentation of acute leukemia using blood and bone marrow smear images: a systematic review

inferred← citeshttps://doi.org/10.1007/s11831-024-10219-yopenalex-snapshot

Transformative Advances in AI for Precise Cancer Detection: A Comprehensive Review of Non-Invasive Techniques

inferred← citeshttps://doi.org/10.1007/s11831-026-10512-yopenalex-snapshot

Next-Generation AI Frameworks for Leukemia Detection: Techniques, Dataset, Challenges, and Future Research Horizons

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/10.1016/j.artmed.2021.102020openalex-snapshot

A survey of deep learning models in medical therapeutic areas

inferred← citeshttps://doi.org/10.1016/j.bspc.2021.103296openalex-snapshot

A classification method to classify bone marrow cells with class imbalance problem

inferred← citeshttps://doi.org/10.1016/j.bspc.2023.105813openalex-snapshot

A blood cell classification method based on MAE and active learning

inferred← citeshttps://doi.org/10.1016/j.engappai.2025.113401openalex-snapshot

Automatic classification of circulating blood cell clusters based on multi-channel flow cytometry imaging

inferred← citeshttps://doi.org/10.1016/j.eswa.2024.124838openalex-snapshot

Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic techniques

inferred← citeshttps://doi.org/10.1016/j.imu.2022.100911openalex-snapshot

Impact of quality, type and volume of data used by deep learning models in the analysis of medical images

inferred← citeshttps://doi.org/10.1016/j.jiph.2020.06.033openalex-snapshot

Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges

inferred← citeshttps://doi.org/10.1038/s41598-023-29331-3openalex-snapshot

A deep learning model for detection of leukocytes under various interference factors

inferred← citeshttps://doi.org/10.1080/10428194.2025.2583449openalex-snapshot

Comprehensive review on learning models of leukemia detection based on morphological information

inferred← citeshttps://doi.org/10.1080/21681163.2023.2219341openalex-snapshot

Deep learning for few-shot white blood cell image classification and feature learning

inferred← citeshttps://doi.org/10.1109/access.2021.3114059openalex-snapshot

Automated Diagnosis of Leukemia: A Comprehensive Review

inferred← citeshttps://doi.org/10.1109/iceca52323.2021.9676121openalex-snapshot

Online and offline hybrid information connection algorithm under the background of “Internet +” for intelligent tutorial system

inferred← citeshttps://doi.org/10.1109/iciccs51141.2021.9432309openalex-snapshot

Astute Segmentation and Classification of leucocytes in blood microscopic smear images using titivated K-means clustering and robust SVM techniques

inferred← citeshttps://doi.org/10.1109/jiot.2025.3603577openalex-snapshot

TFViT: Triplet Focal Vision Transformer Driven by Internet of Medical Things for Leukocyte Classification in Acute Myeloid Leukemia

inferred← citeshttps://doi.org/10.1111/exsy.13225openalex-snapshot

RETRACTED: Segmentation and classification of lymphoblastic leukaemia using quantum neural network

inferred← citeshttps://doi.org/10.1145/3514495openalex-snapshot

Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review

inferred← citeshttps://doi.org/10.1145/3574198.3574201openalex-snapshot

Unsupervised Cross-Domain White Blood Cells Classification Using DANN

inferred← citeshttps://doi.org/10.1515/bmt-2021-0253openalex-snapshot

A method to classify bone marrow cells with rejected option

inferred← citeshttps://doi.org/10.21037/atm-22-486openalex-snapshot

A deep learning method and device for bone marrow imaging cell detection

inferred← citeshttps://doi.org/10.21203/rs.3.rs-2381448/v1openalex-snapshot

Toward Driverless AI: Automating Leukemia Detection and Classification using Hyperautomation, a Case Study

inferred← citeshttps://doi.org/10.3126/sra.v1i1.60140openalex-snapshot

Automated Classification of Genetic Mutations in Cancer using Machine Learning

inferred← citeshttps://doi.org/10.32604/cmes.2025.057462openalex-snapshot

Deep Learning and Artificial Intelligence-Driven Advanced Methods for Acute Lymphoblastic Leukemia Identification and Classification: A Systematic Review

inferred← citeshttps://doi.org/10.3389/fped.2021.693676openalex-snapshot

Development and Evaluation of a Leukemia Diagnosis System Using Deep Learning in Real Clinical Scenarios

inferred← citeshttps://doi.org/10.3390/bios12020047openalex-snapshot

Field-Portable Leukocyte Classification Device Based on Lens-Free Shadow Imaging Technique

inferred← citeshttps://doi.org/10.3390/diagnostics11081373openalex-snapshot

Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images

inferred← citeshttps://doi.org/10.3390/fractalfract9060337openalex-snapshot

Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple Architectures

inferred← citeshttps://doi.org/10.4236/etsn.2021.102003openalex-snapshot

Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning

inferred← citeshttps://doi.org/10.48084/etasr.12790openalex-snapshot

Development of an Automated YOLO-Based Digital Microscopy System for Leukemia Early Detection

inferred← citeshttps://doi.org/10.5120/ijca2022922468openalex-snapshot

Reliability Assessment of Machine Learning in Tumour Detection

inferred← citeshttps://doi.org/10.55041/ijsrem29929openalex-snapshot

BLOOD CELL TYPE DETECTION USING DEEP LEARNING

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