Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Hybrid Deep Learning Based GRU Model for Classifying the Lung Cancer from CT Scan Images Subject The topic of the resource computerized tomography; convolutional network; gated recurrent unit; karhunenloeve; lung cancer detection; otsu thresholding Description An account of the resource Lung cancer is a potentially fatal condition, posing significant challenges for early detection and treatment within the healthcare domain. Despite extensive efforts, the etiology and cure of cancer remain elusive. However, early detection offers hope for effective treatment. This study explores the application of image processing techniques, including noise reduction, feature extraction, and identification of cancerous regions within the lung, augmented by patient medical history data. Leveraging machine learning and image processing, this research presents a methodology for precise lung cancer categorization and prognosis. While computed tomography (CT) scans are a cornerstone of medical imaging, diagnosing cancer solely through CT scans remains challenging even for seasoned medical professionals. The emergence of computer-assisted diagnostics has revolutionized cancer detection and diagnosis. This study utilizes lung images from the Lung Image Database Consortium (LIDC-IDRI) and evaluates various image preprocessing filters such as median, Gaussian, Wiener, Otsu, and rough body area filters. Subsequently, feature extraction employs the Karhunen-Loeve (KL) methodology, followed by lung tumor classification using a hybrid model comprising a One-Dimensional Convolutional Neural Network (1D-CNN) and a Gated Recurrent Unit (GRU). Experimental findings demonstrate that the proposed model achieves a sensitivity of 99.14%, specificity of 90.00%, F -measure of 95.24%, and accuracy of 95%. 2024 IEEE. Creator An entity primarily responsible for making the resource Gondkar R.R.; Gondkar S.R.; Kavitha S.; Siva Balan R.V. Source A related resource from which the described resource is derived 3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDCECE60827.2024.10548266" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDCECE60827.2024.10548266</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196811889&amp;doi=10.1109%2FICDCECE60827.2024.10548266&amp;partnerID=40&amp;md5=550d6668adde0096716614706d9c4db6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196811889&amp;doi=10.1109%2fICDCECE60827.2024.10548266&amp;partnerID=40&amp;md5=550d6668adde0096716614706d9c4db6</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835031860-9 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Gondkar R.R., Christ (Deemed-to-be-University), Department Of Computer Science, Bangalore, India; Gondkar S.R., Bms Institute Of Technology And Management, Department Of Electronic And Communication Engineering, Bangalore, India; Kavitha S., Nitte Meenakshi Institute Of Technology, Department Of Mca, Bangalore, India; Siva Balan R.V., Christ (Deemed-to-be-University), Department Of Computer Science, Bangalore, India