ArticleComputational and mathematical methods in medicine2022
Ensemble Learning Framework with GLCM Texture Extraction for Early Detection of Lung Cancer on CT Images.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 59 citations in OpenAlex.
- WISeRKNet: wide slice residual Kronecker network for lung cancer detection based on CT images.Scientific reports · 2026Article
- Multi-spatial channel attention and inceptionv3-based CAD system with optimized MLP for lung cancer detection.Journal of computer-aided molecular design · 2026Article
- Preoperatively Predicting Risk Stratification for GISTs ≤2 cm by Radiomics Model: A Dual-center StudyCurrent medical imaging · 2026Article
- Assessment of Influencing Factors and Robustness of Computable Image Texture Features in Digital Images.Tomography (Ann Arbor, Mich.) · 2025Article
- Role of Nanoformulations in the Treatment of Lung Cancer.Recent patents on nanotechnology · 2025Review
- Lightweight Advanced Deep Neural Network (DNN) Model for Early-Stage Lung Cancer Detection.Diagnostics (Basel, Switzerland) · 2024Article
- Novel Hybrid Quantum Architecture-Based Lung Cancer Detection Using Chest Radiograph and Computerized Tomography Images.Bioengineering (Basel, Switzerland) · 2024Article
- An Advanced Lung Carcinoma Prediction and Risk Screening Model Using Transfer Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Image Texture Based Classification Methods to Mimic Perceptual Models of Search and Localization in Medical Images.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Efficient lung cancer detection using computational intelligence and ensemble learning.PloS one · 2024Article
- Predictive Value of a Radiomics Nomogram Model Based on Contrast-Enhanced Computed Tomography for KIT Exon 9 Gene Mutation in Gastrointestinal Stromal Tumors.Technology in cancer research & treatmentArticle
- Machine learning-based prediction of mortality in lung cancer: Application of severity-adjustment method.Digital healthArticle
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Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer has emerged as a major cause of death among all demographics worldwide, largely caused by a proliferation of smoking habits. However, early detection and diagnosis of lung cancer through technological improvements can save the lives of millions of individuals affected globally. Computerized tomography (CT) scan imaging is a proven and popular technique in the medical field, but diagnosing cancer with only CT scans is a difficult task even for doctors and experts. This is why computer-assisted diagnosis has revolutionized disease diagnosis, especially cancer detection. This study looks at 20 CT scan images of lungs. In a preprocessing step, we chose the best filter to be applied to medical CT images between median, Gaussian, 2D convolution, and mean. From there, it was established that the median filter is the most appropriate. Next, we improved image contrast by applying adaptive histogram equalization. Finally, the preprocessed image with better quality is subjected to two optimization algorithms, fuzzy c-means and k-means clustering. The performance of these algorithms was then compared. Fuzzy c-means showed the highest accuracy of 98%. The feature was extracted using Gray Level Cooccurrence Matrix (GLCM). In classification, a comparison between three algorithms-bagging, gradient boosting, and ensemble (SVM, MLPNN, DT, logistic regression, and KNN)-was performed. Gradient boosting performed the best among these three, having an accuracy of 90.9%.
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