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dc.contributor.authorTiwari L.
dc.contributor.authorRaja R.
dc.contributor.authorAwasthi V.
dc.contributor.authorMiri R.
dc.contributor.authorSinha G.R.
dc.contributor.authorAlkinani M.H.
dc.contributor.authorPolat K.
dc.date.accessioned2021-06-23T18:57:07Z
dc.date.available2021-06-23T18:57:07Z
dc.date.issued2021
dc.identifier.issn0263-2241
dc.identifier.urihttps://doi.org/10.1016/j.measurement.2020.108882
dc.identifier.urihttps://hdl.handle.net/20.500.12491/5134
dc.description.abstractLung Cancer (LC) is reported as common cause of death all over the world. The detection of cancer can save many lives and help likelihood of survival. Physicians use CT (computed tomography) for examination of the cancer in lung with the help of computer-aided diagnosis (CAD) for efficient detection and diagnosis. The CAD uses different machine learning techniques and signal processing approaches but processing time and accuracy of CAD remains challenging issues. An efficient Deep Learning (DL) methodology is proposed for lung cancer detection utilizing Target based Weighted Elman DL Neural Network (TWEDLNN), and Mask Unit (MU) based 3FCM algorithm. The proposed work includes lung image segmentation using Geometric mean-based Otsu Thresholding (GOT); contrast enhancement (CE) using Modified Clip limit-based Contrasts Limited Adaptive Histograms Equalization (MC-CLAHE); Feature Extraction (FE); Classification of Features using TWEDLNN; and MU based FCM algorithm for LN (lung nodule) detection. We have used CT images of LIDC-IDRI database for the implementation. We have compared the proposed work with existing techniques to confirm that the TWEDLNN detects LC more efficiently and the accuracy of proposed work is also improved as 96%. The performance of proposed MC-CLAHE is authenticated by contrasting the proposed technique's performance with prevailing techniques, CLAHE, Gaussian, Median, and Wiener filters. The proposed method has resulted PSNR of 24.2573 and MSE value of 292.98, which are better than all existing Techniques. © 2020 Elsevier Ltden_US
dc.language.isoengen_US
dc.publisherElsevier B.V.en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCT imagesen_US
dc.subjectDeep Learningen_US
dc.subjectLung Canceren_US
dc.subjectLung Noduleen_US
dc.subjectModified Clip limit-based Contrast Limited Adaptive Histograms Equalization (MC-CLAHE)en_US
dc.subjectTarget Weight based Elman Deep Learning Neural Network (TWEDLNN)en_US
dc.titleDetection of lung nodule and cancer using novel Mask-3 FCM and TWEDLNN algorithmsen_US
dc.typearticleen_US
dc.contributor.department[0-Belirlenecek]en_US
dc.contributor.institutionauthor[0-Belirlenecek]
dc.identifier.doi10.1016/j.measurement.2020.108882
dc.identifier.volume172en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.relation.ispartofMeasurement: Journal of the International Measurement Confederationen_US


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