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Segmentation and classification of lung tumor from 3D CT image using K-means clustering algorithm

https://doi.org/10.1109/icaee.2017.8255451
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What this work says

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

inferredcites →https://doi.org/10.1016/1049-9652(92)90055-3openalex-snapshot
Methods for fast morphological image transforms using bitmapped binary images
inferredcites →https://doi.org/10.1109/embc.2016.7590938openalex-snapshot
Prediction of malignant and benign of lung tumor using a quantitative radiomic method
inferredcites →https://doi.org/10.1109/fuzz-ieee.2016.7737980openalex-snapshot
Lung tumor area recognition in CT images based on Gustafson-Kessel clustering
inferredcites →https://doi.org/10.1109/msmw.2016.7537995openalex-snapshot
Automatic Lung nodule segmentation and classification in CT images based on SVM
inferredcites →https://doi.org/10.1109/socpar.2014.7008037openalex-snapshot
Artificial neural network-based classification system for lung nodules on computed tomography scans
inferredcites →https://doi.org/10.1109/tpami.2002.1017616openalex-snapshot
An efficient k-means clustering algorithm: analysis and implementation
inferredcites →https://doi.org/10.1109/tsmc.1979.4310076openalex-snapshot
A Threshold Selection Method from Gray-Level Histograms
inferredcites →https://doi.org/10.1118/1.3147146openalex-snapshot
Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection
inferredcites →https://doi.org/10.1148/rg.2015140232openalex-snapshot
Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends
inferredcites →https://doi.org/10.3322/caac.21389openalex-snapshot
Head and neck cancers—major changes in the American Joint Committee on cancer eighth edition cancer staging manual
inferredcites →https://doi.org/10.3322/caac.21390openalex-snapshot
Lung cancer — major changes in the American Joint Committee on Cancer eighth edition cancer staging manual
inferredcites →https://doi.org/10.5555/1283383.1283494openalex-snapshot
k-means++: the advantages of careful seeding

What points at this work

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

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Comparative Analysis of Lung Segmentation
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Optimal deep belief network with opposition‐based hybrid grasshopper and honeybee optimization algorithm for lung cancer classification: A DBNGHHB approach
inferred← citeshttps://doi.org/10.1007/978-3-031-59097-9_15openalex-snapshot
Efficient Lung Cancer Segmentation Using Deep Learning-Based Models
inferred← citeshttps://doi.org/10.1007/978-3-032-15977-9_1openalex-snapshot
Introduction of Artificial Intelligence in Healthcare
inferred← citeshttps://doi.org/10.1007/978-981-16-6460-1_48openalex-snapshot
Automatic Detection and Classification of Lung Nodules in CT Images
inferred← citeshttps://doi.org/10.1007/978-981-96-4933-4_1openalex-snapshot
Lung Cancer Stage Estimation Using EfficientNetV2B2: Formulating Gene Expression Data for Comparable ML Outcomes
inferred← citeshttps://doi.org/10.1007/s10278-023-00809-wopenalex-snapshot
An Improved Convolution Neural Network and Modified Regularized K-Means-Based Automatic Lung Nodule Detection and Classification
inferred← citeshttps://doi.org/10.1007/s11042-023-17174-zopenalex-snapshot
Segmentation of 3d medical images for detection and classification of lung tumor using content-based features
inferred← citeshttps://doi.org/10.1007/s40815-022-01399-5openalex-snapshot
A Fusion-Based Convolutional Fuzzy Neural Network for Lung Cancer Classification
inferred← citeshttps://doi.org/10.1016/j.cmpb.2020.105902openalex-snapshot
Optimal Deep Belief Network with Opposition based Pity Beetle Algorithm for Lung Cancer Classification: A DBNOPBA Approach
inferred← citeshttps://doi.org/10.1016/j.future.2018.10.009openalex-snapshot
Optimal deep learning model for classification of lung cancer on CT images
inferred← citeshttps://doi.org/10.1038/s41598-024-65714-wopenalex-snapshot
Classification of brain tumor types through MRIs using parallel CNNs and firefly optimization
inferred← citeshttps://doi.org/10.1038/s41598-025-11574-xopenalex-snapshot
Pyramidal attention-based T network for brain tumor classification: a comprehensive analysis of transfer learning approaches for clinically reliable and reliable AI hybrid approaches
inferred← citeshttps://doi.org/10.1038/s41598-025-14901-4openalex-snapshot
Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis
inferred← citeshttps://doi.org/10.1109/access.2020.2976432openalex-snapshot
Mask R-CNN-Based Detection and Segmentation for Pulmonary Nodule 3D Visualization Diagnosis
inferred← citeshttps://doi.org/10.1109/ic3ecsbhi63591.2025.10991161openalex-snapshot
Comparative Study of Different Models on MRI-Based Brain Tumor Detection
inferred← citeshttps://doi.org/10.1109/icccnt.2018.8494133openalex-snapshot
Automated Detection of Brain Tumor Cells Using Support Vector Machine
inferred← citeshttps://doi.org/10.1109/iceca.2018.8474864openalex-snapshot
Multi-Layer Perceptron Based Lung Tumor Classification
inferred← citeshttps://doi.org/10.1109/icssit46314.2019.8987772openalex-snapshot
Random Forest based Classification Model for Lung Cancer Prediction on Computer Tomography Images
inferred← citeshttps://doi.org/10.1109/iucc/dsci/smartcns.2019.00052openalex-snapshot
An Adaptive Clustering Algorithm of Gaussian Curve Fitting the Histogram Based on K-Means
inferred← citeshttps://doi.org/10.1109/jbhi.2022.3198509openalex-snapshot
A Cascaded Multi-Stage Framework for Automatic Detection and Segmentation of Pulmonary Nodules in Developing Countries
inferred← citeshttps://doi.org/10.1186/s12911-023-02114-6openalex-snapshot
MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques
inferred← citeshttps://doi.org/10.17694/bajece.455132openalex-snapshot
A Convolutional Neural Network Application for Predicting the Locating of Squamous Cell Carcinoma in the Lung
inferred← citeshttps://doi.org/10.2152/jmi.69.244openalex-snapshot
Optimized Deformable Model-based Segmentation and Deep Learning for Lung Cancer Classification
inferred← citeshttps://doi.org/10.21608/jaet.2022.83371.1117openalex-snapshot
Accurate Quantification of Small Pulmonary Nodules Using 3D Reconstruction of 2D Computed Tomography Lung Images
inferred← citeshttps://doi.org/10.3390/cancers15061750openalex-snapshot
Systematic Review of Tumor Segmentation Strategies for Bone Metastases
inferred← citeshttps://doi.org/10.3390/diagnostics10030131openalex-snapshot
A Two-Stage Framework for Automated Malignant Pulmonary Nodule Detection in CT Scans
inferred← citeshttps://doi.org/10.48161/qaj.v1n2a51openalex-snapshot
Medical Images Segmentation Based on Unsupervised Algorithms: A Review
inferred← citeshttps://doi.org/10.5815/ijigsp.2020.01.05openalex-snapshot
Lung Tumor Segmentation and Staging from CT Images Using Fast and Robust Fuzzy C-Means Clustering
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A Systematic Survey of Classification Algorithms for Cancer Detection
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