ArticleJournal of digital imaging2016
A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images.
Article in Journal of digital imaging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it.
- An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images.Journal of medical systems · 2019Pooled it
- An Expert Committee-Driven Adaptive Framework for Multicenter Pulmonary Nodule Classification Using AI-Derived Quantitative CT Features.Journal of imaging informatics in medicine · 2026Article
- Anatomically guided latent diffusion for high-resolution 3D chest CT synthesis.Scientific reports · 2026Article
- Development and validation of a multimodal radiomics-serum biomarker model for diagnosing solid pulmonary nodules via machine learning.Journal of thoracic disease · 2025Article
- Predictive radiomics based ensemble machine learning approach in CT lung nodule diagnosis.Journal of the Egyptian National Cancer Institute · 2025Article
- Article
- Pulmonary Nodule Classification Using a Multiview Residual Selective Kernel Network.Journal of imaging informatics in medicine · 2024Article
- Attention-guided deep neural network with a multichannel architecture for lung nodule classification.Heliyon · 2024Article
- Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter.Journal of cancer research and clinical oncology · 2023Article
- MS-Net: Learning to assess the malignant status of a lung nodule by a radiologist and her peers.Journal of applied clinical medical physics · 2023Article
- Deep-Learning Model of ResNet Combined with CBAM for Malignant-Benign Pulmonary Nodules Classification on Computed Tomography Images.Medicina (Kaunas, Lithuania) · 2023Article
- Article
- A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models.Frontiers in oncology · 2023Article
- Inheritance and innovation of the diagnosis of peripheral pulmonary lesions.Therapeutic advances in chronic disease · 2023Review
- A method for distinguishing benign and malignant pulmonary nodules based on 3D dual path network aided byJournal of Zhejiang University. Science. B · 2022Article
- Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans.Sensors (Basel, Switzerland) · 2022Article
- Lung cancer diagnosis using deep attention-based multiple instance learning and radiomics.Medical physics · 2022Article
- The value of AI in the Diagnosis, Treatment, and Prognosis of Malignant Lung Cancer.Frontiers in radiology · 2022Review
- A brain-like classification method for computed tomography images based on adaptive feature matching dual-source domain heterogeneous transfer learning.Frontiers in human neuroscience · 2022Article
- Application of Recurrent Neural Network Algorithm in Intelligent Detection of Clinical Ultrasound Images of Human Lungs.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Classification of malignant and benign pulmonary nodules is important for further treatment plan. The present work focuses on the classification of benign and malignant pulmonary nodules using support vector machine. The pulmonary nodules are segmented using a semi-automated technique, which requires only a seed point from the end user. Several shape-based, margin-based, and texture-based features are computed to represent the pulmonary nodules. A set of relevant features is determined for the efficient representation of nodules in the feature space. The proposed classification scheme is validated on a data set of 891 nodules of Lung Image Database Consortium and Image Database Resource Initiative public database. The proposed classification scheme is evaluated for three configurations such as configuration 1 (composite rank of malignancy "1" and "2" as benign and "4" and "5" as malignant), configuration 2 (composite rank of malignancy "1","2", and "3" as benign and "4" and "5" as malignant), and configuration 3 (composite rank of malignancy "1" and "2" as benign and "3","4" and "5" as malignant). The performance of the classification is evaluated in terms of area (A z) under the receiver operating characteristic curve. The A z achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 0.9505, 0.8822, and 0.8488, respectively. The proposed method outperforms the most recent technique, which depends on the manual segmentation of pulmonary nodules by a trained radiologist.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.