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Computer-assisted Acute Lymphoblastic Leukemia detection and diagnosis
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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/s00521-013-1438-3openalex-snapshot
An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images
inferredcites →https://doi.org/10.1016/j.artmed.2014.09.002openalex-snapshot
Leucocyte classification for leukaemia detection using image processing techniques
inferredcites →https://doi.org/10.1016/j.beha.2017.07.004openalex-snapshot
Acute lymphoblastic leukemia (ALL)
inferredcites →https://doi.org/10.1109/icip.2011.6115881openalex-snapshot
All-IDB: The acute lymphoblastic leukemia image database for image processing
inferredcites →https://doi.org/10.1109/iembs.2008.4649636openalex-snapshot
Fast histogram equalization for medical image enhancement
inferredcites →https://doi.org/10.1155/2016/9514707openalex-snapshot
Segmentation of White Blood Cell from Acute Lymphoblastic Leukemia Images Using Dual-Threshold Method
inferredcites →https://doi.org/10.1155/2018/6125289openalex-snapshot
Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia
inferredcites →https://doi.org/10.1177/25.7.70454openalex-snapshot
Automatic measurement of sister chromatid exchange frequency.
inferredcites →https://doi.org/10.5281/zenodo.1327858openalex-snapshot
White Blood Cells Identification And Counting From Microscopic Blood Image
inferredcites →https://doi.org/10.9790/0661-16537987openalex-snapshot
Analysis of blood samples for counting leukemia cells using Support vector machine and nearest neighbour
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-030-76732-7_6openalex-snapshot
Microscopic Analysis of Blood Cells for Disease Detection: A Review
inferred← citeshttps://doi.org/10.1007/978-3-030-79276-3_15openalex-snapshot
Detection of Leukemia Using K-Means Clustering and Machine Learning
inferred← citeshttps://doi.org/10.1007/978-3-031-68602-3_12openalex-snapshot
Unlocking the Potential: Machine Learning and Deep Learning in Leukemia Diagnosis with Explainable AI
inferred← citeshttps://doi.org/10.1007/978-3-031-81821-9_1openalex-snapshot
Efficient Detection of Acute Lymphoblastic Leukemia in Microscopic Blood Cell Images
inferred← citeshttps://doi.org/10.1007/s11042-022-13543-2openalex-snapshot
A novel Deep Learning Framework (DLF) for classification of Acute Lymphoblastic Leukemia
inferred← citeshttps://doi.org/10.1007/s11831-022-09776-xopenalex-snapshot
Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades
inferred← citeshttps://doi.org/10.1007/s11831-024-10065-yopenalex-snapshot
Machine Learning and Computer Vision Based Methods for Cancer Classification: A Systematic Review
inferred← citeshttps://doi.org/10.1016/j.aej.2023.01.004openalex-snapshot
An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images
inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106246openalex-snapshot
Knowledge distillation model for Acute Lymphoblastic Leukemia Detection: Exploring the impact of nesterov-accelerated adaptive moment estimation optimizer
inferred← citeshttps://doi.org/10.1016/j.clml.2021.06.025openalex-snapshot
Accurate Machine-Learning-Based classification of Leukemia from Blood Smear Images
inferred← citeshttps://doi.org/10.1016/j.eswa.2021.115311openalex-snapshot
An efficient deep Convolutional Neural Network based detection and classification of Acute Lymphoblastic Leukemia
inferred← citeshttps://doi.org/10.1080/08839514.2021.1995974openalex-snapshot
Automatic Detection of Acute Lymphoblastic Leukemia Using UNET Based Segmentation and Statistical Analysis of Fused Deep Features
inferred← citeshttps://doi.org/10.1088/1742-6596/1372/1/012044openalex-snapshot
Classification of Acute Leukemia Based on Multilayer Perceptron
inferred← citeshttps://doi.org/10.1101/2025.09.17.25336037openalex-snapshot
Leveraging transfer learning from Acute Lymphoblastic Leukemia (ALL) pretraining to enhance Acute Myeloid Leukemia (AML) prediction
inferred← citeshttps://doi.org/10.1109/access.2024.3371490openalex-snapshot
Image Classification of Leukemic Cells Using Invariants of Triangle-Free Graphs as Synthetic Features
inferred← citeshttps://doi.org/10.1109/access.2024.3388715openalex-snapshot
Feature Fusion Based Ensemble of Deep Networks for Acute Leukemia Diagnosis Using Microscopic Smear Images
inferred← citeshttps://doi.org/10.1109/access.2025.3542609openalex-snapshot
LEU3: An Attention Augmented-Based Model for Acute Lymphoblastic Leukemia Classification
inferred← citeshttps://doi.org/10.1109/asiancon55314.2022.9909377openalex-snapshot
Detection and Classification of Various Types of Leukemia Using Image Processing, Transfer Learning and Ensemble Averaging Techniques
inferred← citeshttps://doi.org/10.1109/ecice59523.2023.10383097openalex-snapshot
Chronic Myelogenous Leukemia Detection Using Otsu’s Thresholding and Sobel Edge Detection with AI
inferred← citeshttps://doi.org/10.1109/esci50559.2021.9396781openalex-snapshot
White Blood Cells Classification Using Built-in Customizable Trained Convolutional Neural Network
inferred← citeshttps://doi.org/10.1109/icabme59496.2023.10292943openalex-snapshot
Detection of Chronic Myelogenous Leukemia Applying Support Vector Machine and Feature Extraction
inferred← citeshttps://doi.org/10.1109/iccit68739.2025.11491540openalex-snapshot
Deep Transfer Learning for Hematologic Image Analysis: An Evaluation of Six CNN Architectures for B-Cell Disease Detection
inferred← citeshttps://doi.org/10.1109/iccubea58933.2023.10392196openalex-snapshot
An Automated Framework for Hematologists Level Leukemia Detection Using Blood Cell Images
inferred← citeshttps://doi.org/10.1109/icodt255437.2022.9787449openalex-snapshot
Diagnosis of Leukemia Disease through Deep Learning using Microscopic Images
inferred← citeshttps://doi.org/10.1109/ictacs59847.2023.10390121openalex-snapshot
Detection of Acute Lymphoblastic Leukemia Using CollateNet
inferred← citeshttps://doi.org/10.1109/ies53407.2021.9593939openalex-snapshot
Blood Cells Classification for Identification of Acute Lymphoblastic Leukemia on Microscopic Images Using Image Processing
inferred← citeshttps://doi.org/10.1109/ies59143.2023.10242476openalex-snapshot
Identification of Acute Myeloid Leukemia (AML) Subtypes: M1, M2, M3 on White Blood Cells Using Microscopic Images
inferred← citeshttps://doi.org/10.1109/inmic50486.2020.9318085openalex-snapshot
Efficient Features for Effectively Detection of Leukemia Cells
inferred← citeshttps://doi.org/10.1109/iwaiip58158.2023.10462885openalex-snapshot
Feature Targeted Image Enhancement for Acute Myeloid Leukemia
inferred← citeshttps://doi.org/10.1109/lsens.2023.3345535openalex-snapshot
SBCDNet: An Efficient Sparse-Based Deep Cascade Blood Cancer Detection Network
inferred← citeshttps://doi.org/10.1109/ncc52529.2021.9530010openalex-snapshot
Transfer Learning-Based Automatic Detection of Acute Lymphocytic Leukemia
inferred← citeshttps://doi.org/10.1109/tcbb.2022.3218590openalex-snapshot
An Efficient Detection and Classification of Acute Leukemia Using Transfer Learning and Orthogonal Softmax Layer-Based Model
inferred← citeshttps://doi.org/10.1145/3514495openalex-snapshot
Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review
inferred← citeshttps://doi.org/10.11591/ijece.v12i4.pp3692-3699openalex-snapshot
Acute lymphoblastic leukemia detection approach from peripheral blood smear using color threshold and morphological techniques
inferred← citeshttps://doi.org/10.17488/rmib.43.2.3openalex-snapshot
Comparison of Accuracy of Color Spaces in Cell Features Classification in Images of Leukemia types ALL and MM
inferred← citeshttps://doi.org/10.22266/ijies2021.1231.32openalex-snapshot
Multi Distance and Angle Models of the Gray Level Co-occurrence Matrix (GLCM) to Extract the Acute Lymphoblastic Leukemia (ALL) Images
inferred← citeshttps://doi.org/10.30534/ijeter/2020/1088102020openalex-snapshot
Image Processing Techniques for Acute Leukaemia Detection
inferred← citeshttps://doi.org/10.33166/aetic.2023.05.004openalex-snapshot
Automated Platelet Counter with Detection Using K-Means Clustering
inferred← citeshttps://doi.org/10.3390/app122110760openalex-snapshot
An Efficient Strategy for Blood Diseases Detection Based on Grey Wolf Optimization as Feature Selection and Machine Learning Techniques
inferred← citeshttps://doi.org/10.3390/s22041629openalex-snapshot
Multi-Method Diagnosis of Blood Microscopic Sample for Early Detection of Acute Lymphoblastic Leukemia Based on Deep Learning and Hybrid Techniques
inferred← citeshttps://doi.org/10.3934/mbe.2022093openalex-snapshot
Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia
inferred← citeshttps://doi.org/10.5120/ijca2023922978openalex-snapshot
A Survey on Automated Leukemia Detection and Classification: Exploring Image Processing and Gene Expression Analysis Approaches
inferred← citeshttps://doi.org/10.56726/irjmets37954openalex-snapshot
A SURVEY ON DETECTION OF LEUKEMIA USING IMAGE PROCESSING TECHNIQUES
inferred← citeshttps://doi.org/10.7287/peerj-cs.460v0.1/reviews/2openalex-snapshot
Peer Review #2 of "A review of microscopic analysis of blood cells for disease detection with AI perspective (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.460v0.2/reviews/1openalex-snapshot
Peer Review #1 of "A review of microscopic analysis of blood cells for disease detection with AI perspective (v0.2)"
inferred← citeshttps://doi.org/10.7717/peerj-cs.460openalex-snapshot
A review of microscopic analysis of blood cells for disease detection with AI perspective
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