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dc.contributor.authorShaffie, Ahmed
dc.contributor.authorSoliman, Ahmed
dc.contributor.authorFraiwan, Luay
dc.date.accessioned2019-03-10T08:47:57Z
dc.date.available2019-03-10T08:47:57Z
dc.date.issued2018
dc.identifier.citationhttps://journals.sagepub.com/doi/pdf/10.1177/1533033818798800en_US
dc.identifier.urihttps://dspace.adu.ac.ae/handle/1/1686
dc.descriptionShaffie, A., Soliman, A., Fraiwan, L., Ghazal, M., Taher, F., Dunlap, N., ... & El-Baz, A. (2018). A Generalized Deep Learning-Based Diagnostic System for Early Diagnosis of Various Types of Pulmonary Nodules. Technology in cancer research & treatment, 17, 1533033818798800.en_US
dc.description.abstractA novel framework for the classification of lung nodules using computed tomography scans is proposed in this article. To get an accurate diagnosis of the detected lung nodules, the proposed framework integrates the following 2 groups of features: (1) appearance features modeled using the higher order Markov Gibbs random field model that has the ability to describe the spatial inhomogeneities inside the lung nodule and (2) geometric features that describe the shape geometry of the lung nodules. The novelty of this article is to accurately model the appearance of the detected lung nodules using a new developed seventhorder Markov Gibbs random field model that has the ability to model the existing spatial inhomogeneities for both small and large detected lung nodules, in addition to the integration with the extracted geometric features. Finally, a deep autoencoder classifier is fed by the above 2 feature groups to distinguish between the malignant and benign nodules. To evaluate the proposed framework, we used the publicly available data from the Lung Image Database Consortium. We used a total of 727 nodules that were collected from 467 patients. The proposed system demonstrates the promise to be a valuable tool for the detection of lung cancer evidenced by achieving a nodule classification accuracy of 91.20%en_US
dc.language.isoen_USen_US
dc.publisherSage Publicationen_US
dc.subjectLung canceren_US
dc.subjectComputer-aided diagnosisen_US
dc.subjectHigher order MGRFen_US
dc.subjectAutoencoderen_US
dc.subjectPulmonary noduleen_US
dc.titleA Generalized Deep Learning-Based Diagnostic System for Early Diagnosis of Various Types of Pulmonary Nodulesen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1177/1533033818798800


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