ArticleJournal of digital imaging2020
Classification of CT Scan Images of Lungs Using Deep Convolutional Neural Network with External Shape-Based Features.
Article in Journal of digital imaging, 2020. 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.
- Quantifying morphologic variations as an alternate to standard response criteria for unresectable primary liver tumors after checkpoint inhibition therapy.La Radiologia medica · 2025Article
- Advanced Technologies in Radiation Research.Radiation research · 2024Article
- An Automated Decision Support System to Analyze Malignancy Patterns of Breast Masses Employing Medically Relevant Features of Ultrasound Images.Journal of imaging informatics in medicine · 2024Article
- Multi-Dimension and Multi-Feature Hybrid Learning Network for Classifying the Sub Pathological Type of Lung Nodules through LDCT.Sensors (Basel, Switzerland) · 2021Article
- Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19.Machine vision and applications · 2020Article
- Malignancy pattern analysis of breast ultrasound images using clinical features and a graph convolutional network.Digital healthArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, a simplified yet efficient architecture of a deep convolutional neural network is presented for lung image classification. The images used for classification are computed tomography (CT) scan images obtained from two scientifically used databases available publicly. Six external shape-based features, viz. solidity, circularity, discrete Fourier transform of radial length (RL) function, histogram of oriented gradient (HOG), moment, and histogram of active contour image, have also been identified and embedded into the proposed convolutional neural network. The performance is measured in terms of average recall and average precision values and compared with six similar methods for biomedical image classification. The average precision obtained for the proposed system is found to be 95.26% and the average recall value is found to be 69.56% in average for the two databases.
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Registered trials
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