ArticleJournal of digital imaging2021
Improving the Subtype Classification of Non-small Cell Lung Cancer by Elastic Deformation Based Machine Learning.
Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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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.
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Who cites it
6 citing papers in PubMed.
- Multimodal transformer-enhanced deep learning for accurate assessment of mandibular third molar-inferior alveolar canal contact using panoramic radiography and CBCT slices.BMC oral health · 2026Article
- Automated classification of lung cancer subtypes cells using microscopic images and ensembled deep learning architectures.Scientific reports · 2025Article
- An Automated Diagnosis Method for Lung Cancer Target Detection and Subtype Classification-Based CT Scans.Bioengineering (Basel, Switzerland) · 2024Article
- DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features.Sensors (Basel, Switzerland) · 2024Article
- Performance Analysis of State-of-the-Art CNN Architectures for LUNA16.Sensors (Basel, Switzerland) · 2022Article
- Classification of non-small cell lung cancer by histologic subtype using deep learning in public and private data sets of computed tomography images.Radiologia brasileiraArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-invasive image-based machine learning models have been used to classify subtypes of non-small cell lung cancer (NSCLC). However, the classification performance is limited by the dataset size, because insufficient data cannot fully represent the characteristics of the tumor lesions. In this work, a data augmentation method named elastic deformation is proposed to artificially enlarge the image dataset of NSCLC patients with two subtypes (squamous cell carcinoma and large cell carcinoma) of 3158 images. Elastic deformation effectively expanded the dataset by generating new images, in which tumor lesions go through elastic shape transformation. To evaluate the proposed method, two classification models were trained on the original and augmented dataset, respectively. Using augmented dataset for training significantly increased classification metrics including area under the curve (AUC) values of receiver operating characteristics (ROC) curves, accuracy, sensitivity, specificity, and f
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Registered trials
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