Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/8443
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dc.contributor.authorSavitha, G.
dc.contributor.authorJidesh, P.
dc.date.accessioned2020-03-30T10:18:42Z-
dc.date.available2020-03-30T10:18:42Z-
dc.date.issued2019
dc.identifier.citationAdvances in Intelligent Systems and Computing, 2019, Vol.748, , pp.11-23en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/8443-
dc.description.abstractAn automated computer-aided detection (CAD) system is being proposed for identification of lung nodules present in computed tomography (CT) images. This system is capable of identifying the region of interest (ROI) and extracting the features from the ROI. Feature vectors are generated from the gray-level covariance matrix using the statistical properties of the matrix. The relevant features are identified by adopting principle component analysis algorithm on the feature space (the space formed from the feature vectors). Support vector machine and fuzzy C-means algorithms are used for classifying nodules. Annotated images are used to validate the results. Efficiency and reliability of the system are evaluated visually and numerically using relevant measures. Developed CAD system is found to identify nodules with high accuracy. � Springer Nature Singapore Pte Ltd 2019.en_US
dc.titleLung nodule identification and classification from distorted CT images for diagnosis and detection of lung canceren_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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