{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T07:39:50Z","timestamp":1769931590077,"version":"3.49.0"},"reference-count":24,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Image Grap."],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:p>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%.<\/jats:p>","DOI":"10.1142\/s0219467822400022","type":"journal-article","created":{"date-parts":[[2021,11,10]],"date-time":"2021-11-10T05:22:08Z","timestamp":1636521728000},"source":"Crossref","is-referenced-by-count":16,"title":["Machine Learning with Data Science-Enabled Lung Cancer Diagnosis and Classification Using Computed Tomography Images"],"prefix":"10.1142","volume":"23","author":[{"given":"S.","family":"Vishwa Kiran","sequence":"first","affiliation":[{"name":"Department of AI & ML, BMS Institute of Technology and Management, Bangalore 560064, Karnataka, India"}]},{"given":"Inderjeet","family":"Kaur","sequence":"additional","affiliation":[{"name":"Department of CSE, Ajay Kumar Garg Engineering College, Ghaziabad 201009, Uttar Pradesh, India"}]},{"given":"K.","family":"Thangaraj","sequence":"additional","affiliation":[{"name":"Department of IT, Sona College of Technology, Salem 636005, Tamil Nadu, India"}]},{"given":"V.","family":"Saveetha","sequence":"additional","affiliation":[{"name":"Department of IT, Dr. N. G. P Institute of Technology, Coimbatore 641048, Tamil Nadu, India"}]},{"given":"R.","family":"Kingsy Grace","sequence":"additional","affiliation":[{"name":"Department of CSE, Sri Ramakrishna Engineering College, Coimbatore 641022, Tamil Nadu, India"}]},{"given":"N.","family":"Arulkumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science, CHRIST (Deemed to be University), Bangalore 560029, Karnataka, India"}]}],"member":"219","published-online":{"date-parts":[[2021,11,10]]},"reference":[{"key":"S0219467822400022BIB001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/2055207620914777","volume":"6","author":"Spencer R.","year":"2020","journal-title":"Digital Health"},{"key":"S0219467822400022BIB002","doi-asserted-by":"crossref","first-page":"12241","DOI":"10.1007\/s00500-021-05896-x","volume":"25","author":"Berlin S.","year":"2021","journal-title":"Soft Computing"},{"issue":"5","key":"S0219467822400022BIB003","doi-asserted-by":"crossref","first-page":"2237","DOI":"10.1166\/jctn.2020.8877","volume":"17","author":"Madhan E. 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