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 Articles Article Faculty Publications -Articles 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 Machine Learning with Data Science-Enabled Lung Cancer Diagnosis and Classification Using Computed Tomography Images Subject The topic of the resource data science; disease diagnosis; image processing; Lung cancer; machine learning; predictive models Description An account of the resource In recent times, the healthcare industry has been generating a significant amount of data in distinct formats, such as electronic health records (EHR), clinical trials, genetic data, payments, scientific articles, wearables, and care management databases. Data science is useful for analysis (pattern recognition, hypothesis testing, risk valuation) and prediction. The major, primary usage of data science in the healthcare domain is in medical imaging. At the same time, lung cancer diagnosis has become a hot research topic, as automated disease detection poses numerous benefits. Although numerous approaches have existed in the literature for lung cancer diagnosis, the design of a novel model to automatically identify lung cancer is a challenging task. In this view, this paper designs an automated machine learning (ML) with data science-enabled lung cancer diagnosis and classification (MLDS-LCDC) using computed tomography (CT) images. The presented model initially employs Gaussian filtering (GF)-based pre-processing technique on the CT images collected from the lung cancer database. Besides, they are fed into the normalized cuts (Ncuts) technique where the nodule in the pre-processed image can be determined. Moreover, the oriented FAST and rotated BRIEF (ORB) technique is applied as a feature extractor. At last, sunflower optimization-based wavelet neural network (SFO-WNN) model is employed for the classification of lung cancer. In order to examine the diagnostic outcome of the MLDS-LCDC model, a set of experiments were carried out and the results are investigated in terms of different aspects. The resultant values demonstrated the effectiveness of the MLDS-LCDC model over the other state-of-The-Art methods with the maximum sensitivity of 97.01%, specificity of 98.64%, and accuracy of 98.11%. 2023 World Scientific Publishing Company. Creator An entity primarily responsible for making the resource Vishwa Kiran S.; Kaur I.; Thangaraj K.; Saveetha V.; Kingsy Grace R.; Arulkumar N. Source A related resource from which the described resource is derived International Journal of Image and Graphics, Vol-23, No. 3 Publisher An entity responsible for making the resource available World Scientific Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1142/S0219467822400022" target="_blank" rel="noreferrer noopener">https://doi.org/10.1142/S0219467822400022</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119412118&amp;doi=10.1142%2FS0219467822400022&amp;partnerID=40&amp;md5=394410c12ca853677a192d92375e974b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119412118&amp;doi=10.1142%2fS0219467822400022&amp;partnerID=40&amp;md5=394410c12ca853677a192d92375e974b</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2194678 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 Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Vishwa Kiran S., Department of AI and ML, BMS Institute of Technology and Management, Karnataka, Bangalore, 560064, India; Kaur I., Department of CSE, Ajay Kumar Garg Engineering College, Uttar Pradesh, Ghaziabad, 201009, India; Thangaraj K., Department of IT, Sona College of Technology, Tamil Nadu, Salem, 636005, India; Saveetha V., Department of IT, Dr. N. G. P Institute of Technology, Tamil Nadu, Coimbatore, 641048, India; Kingsy Grace R., Department of CSE, Sri Ramakrishna Engineering College, Tamil Nadu, Coimbatore, 641022, India; Arulkumar N., Department of Computer Science, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560029, India