ArticleJournal of imaging2020
Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features.
Article in Journal of imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Predictive radiomics based ensemble machine learning approach in CT lung nodule diagnosis.Journal of the Egyptian National Cancer Institute · 2025Article
- Lung Segmentation with Lightweight Convolutional Attention Residual U-Net.Diagnostics (Basel, Switzerland) · 2025Article
- A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review.BMC medical imaging · 2024Review
- Tumor segmentation via enhanced area growth algorithm for lung CT images.BMC medical imaging · 2023Article
- ExtRanFS: An Automated Lung Cancer Malignancy Detection System Using Extremely Randomized Feature Selector.Diagnostics (Basel, Switzerland) · 2023Article
- Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection.Computational intelligence and neuroscience · 2023Article
- Performance Analysis of State-of-the-Art CNN Architectures for LUNA16.Sensors (Basel, Switzerland) · 2022Article
- Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges.Journal of personalized medicine · 2022Review
- VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images.Diagnostics (Basel, Switzerland) · 2021Article
- Joint segmentation and detection of COVID-19 via a sequential region generation network.Pattern recognition · 2021Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The lung tumor is among the most detrimental kinds of malignancy. It has a high occurrence rate and a high death rate, as it is frequently diagnosed at the later stages. Computed Tomography (CT) scans are broadly used to distinguish the disease; computer aided systems are being created to analyze the ailment at prior stages productively. In this paper, we present a fully automatic framework for nodule detection from CT images of lungs. A histogram of the grayscale CT image is computed to automatically isolate the lung locale from the foundation. The results are refined using morphological operators. The internal structures are then extracted from the parenchyma. A threshold-based technique is proposed to separate the candidate nodules from other structures, e.g., bronchioles and blood vessels. Different statistical and shape-based features are extracted for these nodule candidates to form nodule feature vectors which are classified using support vector machines. The proposed method is evaluated on a large lungs CT dataset collected from the Lung Image Database Consortium (LIDC). The proposed method achieved excellent results compared to similar existing methods; it achieves a sensitivity rate of 93.75%, which demonstrates its effectiveness.
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