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Automated identification of normoblast cell from human peripheral blood smear images

https://doi.org/10.1111/jmi.12640
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What this work says

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

inferredcites →https://doi.org/10.1016/0098-3004(84)90020-7openalex-snapshot
FCM: The fuzzy c-means clustering algorithm
inferredcites →https://doi.org/10.1016/j.artmed.2011.05.005openalex-snapshot
A supervised method to assist the diagnosis and monitor progression of Alzheimer's disease using data from an fMRI experiment
inferredcites →https://doi.org/10.1016/j.compmedimag.2005.10.001openalex-snapshot
Fuzzy c-means clustering with spatial information for image segmentation
inferredcites →https://doi.org/10.1016/j.micron.2010.04.017openalex-snapshot
Automated leukocyte recognition using fuzzy divergence
inferredcites →https://doi.org/10.1016/j.micron.2013.10.008openalex-snapshot
Development of hedge operator based fuzzy divergence measure and its application in segmentation of chronic myelogenous leukocytes from microscopic image of peripheral blood smear
inferredcites →https://doi.org/10.1016/j.micron.2013.12.001openalex-snapshot
Automatic leukocyte nucleus segmentation by intuitionistic fuzzy divergence based thresholding
inferredcites →https://doi.org/10.1016/j.micron.2014.01.004openalex-snapshot
Accurate segmentation of leukocyte in blood cell images using Atanassov's intuitionistic fuzzy and interval Type II fuzzy set theory
inferredcites →https://doi.org/10.1016/j.micron.2014.04.001openalex-snapshot
Automated microscopic image analysis for leukocytes identification: A survey
inferredcites →https://doi.org/10.1016/j.patrec.2008.10.003openalex-snapshot
Optimal multi-level thresholding using a two-stage Otsu optimization approach
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1038/srep14938openalex-snapshot
An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images
inferredcites →https://doi.org/10.1080/21681163.2014.897650openalex-snapshot
Automated morphometric classification of acute lymphoblastic leukaemia in blood microscopic images using an ensemble of classifiers
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/iecr.2010.5720171openalex-snapshot
Image analysis of blood microscopic images for acute leukemia detection
inferredcites →https://doi.org/10.1109/ihci.2012.6481865openalex-snapshot
Kernel Induced Rough c-means clustering for lymphocyte image segmentation
inferredcites →https://doi.org/10.1109/ist.2011.5962188openalex-snapshot
Unsupervised colour segmentation of white blood cell for acute leukaemia images
inferredcites →https://doi.org/10.1111/j.1749-6632.1965.tb11715.xopenalex-snapshot
THE ANALYSIS OF CELL IMAGES*
inferredcites →https://doi.org/10.1111/jmi.12206openalex-snapshot
Automated system for characterization and classification of malaria‐infected stages using light microscopic images of thin blood smears
inferredcites →https://doi.org/10.1111/jmi.12270openalex-snapshot
Computational microscopic imaging for malaria parasite detection: a systematic review
inferredcites →https://doi.org/10.1201/9781420079517openalex-snapshot
Digital Image Processing
inferredcites →https://doi.org/10.1201/b13729-20openalex-snapshot
Textural Pattern Classification of Microscopic Images for Malaria Screening

What points at this work

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

inferred← citeshttps://doi.org/10.1002/jbio.202500121openalex-snapshot
Unstained Blood Smear Analysis: A Review of Rule‐Based, Machine Learning, and Deep Learning Techniques
inferred← citeshttps://doi.org/10.1007/s11517-023-02955-3openalex-snapshot
CDC-NET: a cell detection and confirmation network of bone marrow aspirate images for the aided diagnosis of AML
inferred← citeshttps://doi.org/10.1016/j.bspc.2023.105243openalex-snapshot
ROI-BMC-DNNet: An efficient automatic analysis model of whole-slide scanned bone marrow aspirate images for the diagnosis of hematological disorders
inferred← citeshttps://doi.org/10.1016/j.compmedimag.2020.101813openalex-snapshot
Automatic identification of abnormal blood smear images using color and morphology variation of RBCS and central pallor
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.126725openalex-snapshot
White blood cell classification using multi-hop attention graph neural networks
inferred← citeshttps://doi.org/10.1016/j.tice.2018.06.004openalex-snapshot
Automatic identification of clinically relevant regions from oral tissue histological images for oral squamous cell carcinoma diagnosis
inferred← citeshttps://doi.org/10.1109/eiecs53707.2021.9587972openalex-snapshot
The Holographic Reconstruction Algorithm Suitable For Real-time Monitoring Of Biological Cells
inferred← citeshttps://doi.org/10.1109/tem.2021.3103549openalex-snapshot
Peripheral Blood Smear Analysis Using Automated Computer-Aided Diagnosis System to Identify Acute Myeloid Leukemia
inferred← citeshttps://doi.org/10.1155/2020/4015323openalex-snapshot
Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images
inferred← citeshttps://doi.org/10.32604/cmc.2022.019115openalex-snapshot
Malaria Parasite Detection Using a Quantum-Convolutional Network
inferred← citeshttps://doi.org/10.3390/app12105030openalex-snapshot
Identification of Anemia and Its Severity Level in a Peripheral Blood Smear Using 3-Tier Deep Neural Network
inferred← citeshttps://doi.org/10.7717/peerj-cs.1813openalex-snapshot
Blood cell image segmentation and classification: a systematic review
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