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dc.contributor.authorKadry, Seifedine
dc.contributor.authorHerrera-Viedma, Enrique
dc.contributor.authorGonzález-Crespo, Rubén
dc.contributor.authorKrishnamoorthy, Sujatha
dc.contributor.authorRajinikanth, Venkatesan
dc.date2022
dc.date.accessioned2023-09-18T06:42:31Z
dc.date.available2023-09-18T06:42:31Z
dc.identifier.citationKadry, S., Herrera-Viedma, E., Crespo, R. G., Krishnamoorthy, S., & Rajinikanth, V. (2023). Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study. Procedia Computer Science, 218, 2786-2794.es_ES
dc.identifier.issn1877-0509
dc.identifier.urihttps://reunir.unir.net/handle/123456789/15279
dc.description.abstractThe lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases; (i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods.es_ES
dc.language.isoenges_ES
dc.publisherProcedia Computer Sciencees_ES
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S1877050923002508?via%3Dihubes_ES
dc.rightsopenAccesses_ES
dc.subjectaccuracyes_ES
dc.subjectCNN segmentationes_ES
dc.subjectCT scanes_ES
dc.subjectdetectiones_ES
dc.subjectlung nodulees_ES
dc.subjectVGG-SegNetes_ES
dc.subjectScopus(2)es_ES
dc.titleAutomatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A studyes_ES
dc.typeconferenceObjectes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.1016/j.procs.2023.01.250


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