Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Improved Classification of Lung Cancer Using Radial Basis Function Neural Network with Affine Transforms of Voss Representation.

PloS one - 1 Jan 2015

Adetiba Emmanuel, Olugbara Oludayo O

Abstract excerpt

Lung cancer is one of the diseases responsible for a large number of cancer related death cases worldwide. The recommended standard for screening and early detection of lung cancer is the low dose computed tomography. However, many patients diagnosed die within one year, which makes it essential to find alternative approaches for screening and early detection of lung cancer. We present computational methods that...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Biomarkers, Tumor
Share to Databases, Genetic
Share to Genomics

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Lung Neoplasms

Share to Mutation

Share to Neural Networks, Computer

Source

Open in PubMed Open full text

Identifiers

PMID: 26625358
DOI: 10.1371/journal.pone.0143542
PMCID: PMC4666594

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Improved Classification of Lung Cancer Using Radial Basis Function Neural Network with Affine Transforms of Voss Representation.PMID 26625358Read ↗

Lung cancer prediction using neural network ensemble with histogram of oriented gradient genomic features.Semantic proximity Read ↗

Prediction of Target-Drug Therapy by Identifying Gene Mutations in Lung Cancer With Histopathological Stained Image and Deep Learning TechniquesSemantic proximity Read ↗

Predicting EGFR gene mutation status in lung adenocarcinoma based on multifeature fusionSemantic proximity Read ↗

Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung AdenocarcinomaSemantic proximity Read ↗

Developing Digital Twin Modelling for Lung Cancer Management Powered by Deep Learning-based Survival Analysis, Risk Stratification and Decision Support RecommendationsSemantic proximity Read ↗

On the way for the best imaging features from CT images to predict EGFR Mutation Status in Lung Cancer.Semantic proximity Read ↗

CNNcon: A Quantitative Imaging Tool for Lung CT Image Feature AnalysisSemantic proximity Read ↗

Topological radiogenomics based on persistent lifetime images for identification of epidermal growth factor receptor mutation in patients with non-small cell lung tumors.Semantic proximity Read ↗

Advancing Lung Cancer Classification through Machine Learning: A Comprehensive Comparative Analysis of Model PerformanceSemantic proximity Read ↗

Using Vision Transformer for high robustness and generalization in predicting EGFR mutation status in lung adenocarcinoma.Semantic proximity Read ↗

Pneumonia Screening From Radiology Images Using Homomorphic Transformation Filter‐Based FAWT and Customized VGG ‐16Semantic proximity Read ↗

Integration of CA attention and KAN algorithm to predict EGFR mutation status in lung cancerSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.