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Multiclass blood cancer classification using deep CNN with optimized features

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1 of the 37 references listed on this page is in the Retraction Watch database as retracted (export 2026-11-11): 10.1155/2022/1467070 (notice 2023-08-02).

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What this work says

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

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inferredcites →https://doi.org/10.1002/jemt.23139openalex-snapshot
Classification of acute lymphoblastic leukemia using deep learning
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
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Machine aided malaria parasitemia detection in Giemsa-stained thin blood smears
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Acute lymphoid leukemia etiopathogenesis
inferredcites →https://doi.org/10.1007/s12652-020-02463-4openalex-snapshot
Deep learning based facial expression recognition using improved Cat Swarm Optimization
inferredcites →https://doi.org/10.1016/j.asoc.2018.11.001openalex-snapshot
GA-SVM based feature selection and parameter optimization in hospitalization expense modeling
inferredcites →https://doi.org/10.1016/j.bspc.2018.08.012openalex-snapshot
Texture feature based classification on microscopic blood smear for acute lymphoblastic leukemia detection
inferredcites →https://doi.org/10.1016/j.commatsci.2021.110490openalex-snapshot
Building Machine Learning systems for multi-atoms structures: CH3NH3PbI3 perovskite nanoparticles
inferredcites →https://doi.org/10.1016/j.jtho.2021.01.1616openalex-snapshot
Biomarker Discovery and Validation: Statistical Considerations
inferredcites →https://doi.org/10.1016/j.patrec.2019.11.017openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.procs.2015.08.082openalex-snapshot
Automated Leukaemia Detection Using Microscopic Images
inferredcites →https://doi.org/10.1080/02564602.2014.906861openalex-snapshot
Medical Image Segmentation Methods, Algorithms, and Applications
inferredcites →https://doi.org/10.1109/access.2022.3196037openalex-snapshot
A Systematic Review on Recent Advancements in Deep and Machine Learning Based Detection and Classification of Acute Lymphoblastic Leukemia
inferredcites →https://doi.org/10.1109/hydcon48903.2020.9242745openalex-snapshot
Detection and Classification of Acute Lymphocytic Leukemia
inferredcites →https://doi.org/10.1140/epjp/i2017-11523-8openalex-snapshot
Bi-model processing for early detection of breast tumor in CAD system
inferredcites →https://doi.org/10.1155/2021/5478157openalex-snapshot
Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence‐Oriented Deep Learning Methods
inferredcites →https://doi.org/10.1155/2021/7529893openalex-snapshot
Method for Diagnosis of Acute Lymphoblastic Leukemia Based on ViT‐CNN Ensemble Model
inferredcites →https://doi.org/10.1155/2022/1467070openalex-snapshot
[Retracted] Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data
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A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection
inferredcites →https://doi.org/10.11591/eei.v9i2.1857openalex-snapshot
Investigation of white blood cell biomaker model for acute lymphoblastic leukemia detection based on convolutional neural network
inferredcites →https://doi.org/10.1177/1748301818756225openalex-snapshot
Prediction of Benign and Malignant Breast Cancer Using Data Mining Techniques
inferredcites →https://doi.org/10.1186/s12885-018-4638-5openalex-snapshot
Removal of pectoral muscle based on topographic map and shape-shifting silhouette
inferredcites →https://doi.org/10.18280/ria.340304openalex-snapshot
Performance Evaluation of Machine Learning for Recognizing Human Facial Emotions
inferredcites →https://doi.org/10.22266/ijies2022.1031.54openalex-snapshot
Classification of Acute Lymphoblastic Leukemia on White Blood Cell Microscopy Images Based on Instance Segmentation Using Mask R-CNN
inferredcites →https://doi.org/10.30534/ijatcse/2020/12922020openalex-snapshot
Breast Cancer Detection Using Machine Learning
inferredcites →https://doi.org/10.3390/app112210662openalex-snapshot
An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
inferredcites →https://doi.org/10.3390/diagnostics13030404openalex-snapshot
Computer-Aided Diagnosis System for Blood Diseases Using EfficientNet-B3 Based on a Dynamic Learning Algorithm
inferredcites →https://doi.org/10.3390/diagnostics9030104openalex-snapshot
Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network
inferredcites →https://doi.org/10.3390/electronics10060699openalex-snapshot
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inferredcites →https://doi.org/10.3390/electronics12020322openalex-snapshot
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inferredcites →https://doi.org/10.3390/healthcare10101812openalex-snapshot
Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images
inferredcites →https://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
inferredcites →https://doi.org/10.3934/mbe.2020328openalex-snapshot
Brain tumor classification in MRI image using convolutional neural network

What points at this work

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

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Learning Architecture for Brain Tumor Classification Based on Deep Convolutional Neural Network: Classic and Resnet50
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Learning Architecture for Brain Tumor Classification Based on Deep Convolutional Neural Network: Classic and Resnet50
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Lymphoma Classification Using Convolutional Neural Network With Dense Attention Learning Strategy
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Deep Learning and Machine Learning Models Across Domains: A Survey of CNN, XGBoost, and Transformers
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In-Depth Analysis of Deep Learning Approaches for Acute Lymphoblastic Leukemia on Blood Smear Images
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A Performance Comparison of Different YOLOv7 Networks for High-Accuracy Cell Classification in Bronchoalveolar Lavage Fluid Utilising the Adam Optimiser and Label Smoothing
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Advanced Hybridization and Optimization of DNNs for Medical Imaging: A Survey on Disease Detection Techniques
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Transformative Advances in AI for Precise Cancer Detection: A Comprehensive Review of Non-Invasive Techniques
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/s13198-025-02843-zopenalex-snapshot
A novel leukemic cells feature selection using convolutional neural networks and classification using machine learning algorithms
inferred← citeshttps://doi.org/10.1007/s41870-024-02126-zopenalex-snapshot
VisTA: vision transformer-attention enhanced CNN ensemble for optimized classification of acute lymphoblastic leukemia benign and progressive malignant stages
inferred← citeshttps://doi.org/10.1016/j.ajpath.2026.03.011openalex-snapshot
Diagnosis of Leukemia from Bone Marrow Flow Cytometry Data Using Deep Learning and Explainable Artificial Intelligence
inferred← citeshttps://doi.org/10.1016/j.artmed.2024.102883openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2025.108645openalex-snapshot
Efficient lightweight CNN for automated classification of B-cell acute lymphoblastic leukemia
inferred← citeshttps://doi.org/10.1016/j.dib.2024.110153openalex-snapshot
BDWaste: A comprehensive image dataset of digestible and indigestible waste in Bangladesh
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.113676openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.heliyon.2024.e32400openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-023-44210-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-024-75964-3openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-025-23497-8openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-025-29672-1openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-026-51619-3openalex-snapshot
Metaheuristic optimization of deep CNNs for multi-class diagnosis of cervical cancer and lymphoma
inferred← citeshttps://doi.org/10.1055/s-0046-1819717openalex-snapshot
Deep DenseNet Convolution Generative Neural Network to Identify Leukemia
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Optimised convolutional neural network model for multiclass acute lymphoblastic leukemia classification in in peripheral blood smear images
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inferred← citeshttps://doi.org/10.1109/access.2025.3542609openalex-snapshot
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Acute Lymphoblastic Leukemia Detection from Smear Images Using Deep Learning: A Review
inferred← citeshttps://doi.org/10.1109/dasa63652.2024.10836321openalex-snapshot
Blood Cell Cancer Classification Using ResNet50 and Transfer Learning: Enhancing Diagnostic Accuracy in Hematology
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Cascaded Deep CNN Network for Breast Cancer Screening from Ultrasound Images Using Differential Evolution Algorithm
inferred← citeshttps://doi.org/10.1109/gccit63234.2024.10862090openalex-snapshot
Revolutionizing Leukemia Diagnosis: Fine-Tuned VGG19 CNN for Enhanced Classification Accuracy
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Enhanced Blood Cell Classification Using Convolutional Neural Networks for Accurate Diagnosis of Haematological Conditions
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An Efficient Fine-tuned GoogleNet Model for Multiclass Classification of Blood Cell Cancer
inferred← citeshttps://doi.org/10.1109/icaect68478.2026.11426015openalex-snapshot
Hyperfused Multi-Modal Deep Learning for Automated Acute Lymphoblastic Leukemia Detection and Subtype Classification
inferred← citeshttps://doi.org/10.1109/icaiihi67124.2025.11403052openalex-snapshot
Breast Cancer Prediction from Genes using CSO and PSO Tuned Long Short-Term Memory
inferred← citeshttps://doi.org/10.1109/iccae64891.2025.10980547openalex-snapshot
Bee Colony-Reptile Search Optimization Technique for Blood Cell Cancer Detection
inferred← citeshttps://doi.org/10.1109/icce67443.2026.11449637openalex-snapshot
LeukoLite: A Lightweight and Robust CNN for Smartphone-Based Leukemia Diagnosis
inferred← citeshttps://doi.org/10.1109/iccece58645.2024.10497341openalex-snapshot
Comparative Analysis of Diverse Architectures for Accurate Blood Cancer Cell Classification
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Automated Diagnosis of Acute Lymphoblastic Leukemia Leveraging EfficientNet and Fastai
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Hybrid Xce-DenseNet121 Model for Diagnosis of Acute Lymphoblastic Leukemia Integrated with XAI
inferred← citeshttps://doi.org/10.1109/iccrtee68719.2026.11566213openalex-snapshot
Optimized Deep Learning Framework for Blood Cancer Diagnosis
inferred← citeshttps://doi.org/10.1109/iccubea65967.2025.11283764openalex-snapshot
Precise Blood Cancer Detection Using GANs and Cross-Modality Data Integration for Optimal Diagnostics
inferred← citeshttps://doi.org/10.1109/icdici66477.2025.11135298openalex-snapshot
AI-Powered Classification of Bone Marrow Cancer: A Deep Learning Approach for Leukemia and Myeloma Diagnosis
inferred← citeshttps://doi.org/10.1109/icdt63985.2025.10986330openalex-snapshot
Performance Analysis of Optimizers on Convolutional Neural Network for Lung Cancer Classification Using CT Scan Images
inferred← citeshttps://doi.org/10.1109/iciccs65191.2025.10985277openalex-snapshot
Hybrid CNN-LSTM with Clahe-Enhanced Segmentation for Automated Leukemia Detection
inferred← citeshttps://doi.org/10.1109/icitsif69060.2026.11608961openalex-snapshot
Hybrid Deep Learning Ensemble Framework for Early Detection and Classification of Blood Cancer
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Fusion of Local Convolutional Features and Global Attention for Automated Leukemia Cell Detection
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Leveraging Hybrid Deep Learning for Effective Multiclass Blood Cancer Detection
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Blood Cancer Detection Using Machine Learning: A Review
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Hybrid Transformer CNN Model for Blood Cancer Detection with Explainable Decision Visualization
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Javanese Script Single Letters Classification using GoogLeNet Architecture and Adam Optimizer Based on Convolutional Neural Networks (CNN)
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Contrastive Representation Learning for Acute Lymphoblastic Leukemia: A SimCLR and EfficientNet-B0 Approach
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Hybrid AI Classification Techniques for Multi-Class Leukemia Sub-Types Classification with Bone Marrow Cells
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A Novel Hybrid Xception-DenseNet121 Model for Acute Lymphoblastic Leukemia Classification
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A Novel Deep Learning Framework for Enhanced Acute Lymphoblastic Leukemia Detection
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Ocotillo optimization-driven deep learning for bone marrow cytology classification
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Design and development of a convolutional neural network based on human cognitive attention mechanism for automatic classification of leukemia
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Enhancing face recognition privacy through the integration of differential privacy and convolutional neural network
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The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning
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Prediction of Breast Cancer Diseases From Genes Using Pso and Cso Tuned Long Short-term Memory
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Determining Lung Cancer Using Convolutional Neural Networks
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Advancing Early Leukemia Diagnostics: A Comprehensive Study Incorporating Image Processing and Transfer Learning
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Learning Architecture for Brain Tumor Classification Based on Deep Convolutional Neural Network: Classic and ResNet50
inferred← citeshttps://doi.org/10.3390/fractalfract9060337openalex-snapshot
Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple Architectures
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A Comprehensive Assessment and Classification of Acute Lymphocytic Leukemia
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Utilizing Deep Feature Fusion for Automatic Leukemia Classification: An Internet of Medical Things-Enabled Deep Learning Framework
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A Hybrid Deep and Machine Learning Framework with Feature Selection for Automated Classification of Acute Lymphoblastic Leukemia
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Deep Learning for Lung Cancer Staging: A Performance Evaluation of Gamma Correction vs Histogram Equalization Using EfficientNetB0
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CLASSIFICATION OF WBC USING CONVOLUTION NEURAL NETWORKS
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A Comparison of Convolutional Neural Networks, Image Feature Extraction, and Clinical Features Applied to Leukemia Blood Smear Image Classification
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ALL-Net: integrating CNN and explainable-AI for enhanced diagnosis and interpretation of acute lymphoblastic leukemia
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