ArticleHeliyon2024
Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- LungCraft: a hybrid 3D-2D deep learning and radiomics framework with explainable AI for precision diagnosis of lung cancer.Frontiers in artificial intelligence · 2026Article
- Predicting and classifying type 2 diabetes using a transparent ensemble model combining random forest, k-nearest neighbor, and neural networks.Scientific reports · 2025Article
- Enhanced Diagnosis of Lung and Colon Cancer Severity Through Deep Feature Analysis Using DenseNet201 and SVM With Histopathological Images: A Super-Resolution Approach.Cancer reports (Hoboken, N.J.) · 2025Article
- Explainable self-supervised learning for medical image diagnosis based on DINO V2 model and semantic search.Scientific reports · 2025Article
- Lung Cancer Management: Revolutionizing Patient Outcomes Through Machine Learning and Artificial Intelligence.Cancer reports (Hoboken, N.J.) · 2025Article
- A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction.Medical & biological engineering & computing · 2024Review
- A Lightweight Dual-Output Vision Transformer for Enhanced Lung Nodule Classification Using CT Images.Technology in cancer research & treatmentArticle
Corrections and comments
- Retraction · 2025-10-03Concerns/Issues about Referencing/Attributions · Investigation by Journal/Publisher · Objections by Author(s) · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning offers significant potential for lung cancer detection, enabling early diagnosis and potentially improving patient outcomes. Feature extraction remains a crucial challenge in this domain. Combining the most relevant features can further enhance detection accuracy. This study employed a hybrid feature extraction approach, which integrates both Gray-level co-occurrence matrix (GLCM) with Haralick and autoencoder features with an autoencoder. These features were subsequently fed into supervised machine learning methods. Support Vector Machine (SVM) Radial Base Function (RBF) and SVM Gaussian achieved perfect performance measures, while SVM polynomial produced an accuracy of 99.89% when utilizing GLCM with an autoencoder, Haralick, and autoencoder features. SVM Gaussian achieved an accuracy of 99.56%, while SVM RBF achieved an accuracy of 99.35% when utilizing GLCM with Haralick features. These results demonstrate the potential of the proposed approach for developing improved diagnostic and prognostic lung cancer treatment planning and decision-making systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.