ArticleCancers2023
A Series-Based Deep Learning Approach to Lung Nodule Image Classification.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- An intelligent lung nodule classification model using 3D Trans-DenseUnet++-based lung nodule segmentation.Scientific reports · 2026Article
- Observational
- Predictive radiomics based ensemble machine learning approach in CT lung nodule diagnosis.Journal of the Egyptian National Cancer Institute · 2025Article
- Automated pulmonary nodule classification from low-dose CT images using ERBNet: an ensemble learning approach.Medical & biological engineering & computing · 2025Article
- Medical rationing choices of laypeople and clinicians are often illogical and inconsistent with their own stated preferences.PloS one · 2025Article
- Article
- EDTNet: A spatial aware attention-based transformer for the pulmonary nodule segmentation.PloS one · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although many studies have shown that deep learning approaches yield better results than traditional methods based on manual features, CADs methods still have several limitations. These are due to the diversity in imaging modalities and clinical pathologies. This diversity creates difficulties because of variation and similarities between classes. In this context, the new approach from our study is a hybrid method that performs classifications using both medical image analysis and radial scanning series features. Hence, the areas of interest obtained from images are subjected to a radial scan, with their centers as poles, in order to obtain series. A U-shape convolutional neural network model is then used for the 4D data classification problem. We therefore present a novel approach to the classification of 4D data obtained from lung nodule images. With radial scanning, the eigenvalue of nodule images is captured, and a powerful classification is performed. According to our results, an accuracy of 92.84% was obtained and much more efficient classification scores resulted as compared to recent classifiers.
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Registered trials
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