{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T00:06:48Z","timestamp":1725322008206},"reference-count":75,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1016\/j.bspc.2023.105543","type":"journal-article","created":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T17:33:26Z","timestamp":1697218406000},"page":"105543","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"special_numbering":"PA","title":["Texture and Radiomics inspired Data-Driven Cancerous Lung Nodules Severity Classification"],"prefix":"10.1016","volume":"88","author":[{"given":"Himanshu","family":"Gupta","sequence":"first","affiliation":[]},{"given":"Himanshu","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Anil","family":"Kumar","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2023.105543_b0005","unstructured":"What Is Cancer? - NCI. https:\/\/www.cancer.gov\/about-cancer\/understanding\/what-is-cancer (accessed June 7, 2022)."},{"key":"10.1016\/j.bspc.2023.105543_b0010","unstructured":"Types of cancer | Cancer research | What is cancer?. https:\/\/home.cancerresearch\/types-of-cancer\/ (accessed June 7, 2022)."},{"key":"10.1016\/j.bspc.2023.105543_b0015","unstructured":"Cancer. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cancer (accessed September 15, 2022)."},{"key":"10.1016\/j.bspc.2023.105543_b0020","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1016\/j.jtho.2021.02.004","article-title":"Lung cancer in india","volume":"16","author":"Singh","year":"2021","journal-title":"Journal of Thoracic Oncology"},{"key":"10.1016\/j.bspc.2023.105543_b0025","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12885-022-09578-1","article-title":"Burden of cancers in India - estimates of cancer crude incidence, YLLs, YLDs and DALYs for 2021 and 2025 based on National Cancer Registry Program","volume":"22","author":"Kulothungan","year":"2022","journal-title":"BMC Cancer"},{"key":"10.1016\/j.bspc.2023.105543_b0030","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1259\/0007-1285-46-552-1016","article-title":"Computerized transverse axial scanning (tomography): I. Description of system","volume":"46","author":"Hounsfield","year":"1973","journal-title":"The British Journal of Radiology"},{"key":"10.1016\/j.bspc.2023.105543_b0035","series-title":"Principles of radiographic imaging: an art and a science","author":"Carlton","year":"2019"},{"key":"10.1016\/j.bspc.2023.105543_b0040","series-title":"Radiographic photography and imaging processes","author":"Jenkins","year":"2012"},{"key":"10.1016\/j.bspc.2023.105543_b0045","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1016\/S0140-6736(97)08229-9","article-title":"Mass screening for lung cancer with mobile spiral computed tomography scanner","volume":"351","author":"Sone","year":"1998","journal-title":"Lancet"},{"key":"10.1016\/j.bspc.2023.105543_b0050","doi-asserted-by":"crossref","first-page":"740","DOI":"10.6004\/jnccn.2010.0056","article-title":"Non-small cell lung cancer: Clinical practice guidelines in oncology","volume":"8","author":"Ettinger","year":"2010","journal-title":"JNCCN J. Natl. Compr. Cancer Netw."},{"key":"10.1016\/j.bspc.2023.105543_b0055","doi-asserted-by":"crossref","DOI":"10.1097\/00005382-199521000-00001","article-title":"Lung Nodules: Enchantment with Enhancement","volume":"10","author":"Swensen","year":"1995","journal-title":"Journal of Thoracic Imaging"},{"key":"10.1016\/j.bspc.2023.105543_b0060","unstructured":"D.A. Clunie, Medical Image Format FAQ, (2005). https:\/\/www.dclunie.com\/medical-image-faq\/html\/part1.html (accessed June 7, 2022)."},{"key":"10.1016\/j.bspc.2023.105543_b0065","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1117\/12.967615","article-title":"Filmless picture archiving and communication in diagnostic radiology, 1st Intl Conf Work","volume":"0318","author":"Duerinckx","year":"1982","journal-title":"Pict. Arch. Commun. Syst."},{"key":"10.1016\/j.bspc.2023.105543_b0070","doi-asserted-by":"crossref","first-page":"4","DOI":"10.4103\/0971-3026.95396","article-title":"Managing DICOM images: Tips and tricks for the radiologist","volume":"22","author":"Varma","year":"2012","journal-title":"Indian J. Radiol Imaging."},{"key":"10.1016\/j.bspc.2023.105543_b0075","unstructured":"DICOM, About DICOM: Overview, Med. Imaging Technol. Assoc. (2021). https:\/\/www.dicomstandard.org\/about (accessed June 7, 2022)."},{"key":"10.1016\/j.bspc.2023.105543_b0080","unstructured":"DICOM PS3.1 2022b - Introduction and Overview, (2022). https:\/\/www.dicomstandard.org\/standards\/view\/introduction-overview#sect_1.3 (accessed June 7, 2022)."},{"key":"10.1016\/j.bspc.2023.105543_b0085","series-title":"Brain Mapp. Methods","first-page":"427","article-title":"CT angiography and CT perfusion imaging","author":"Lev","year":"2002"},{"key":"10.1016\/j.bspc.2023.105543_b0090","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: Extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"European Journal of Cancer"},{"key":"10.1016\/j.bspc.2023.105543_b0095","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1016\/j.mri.2012.06.010","article-title":"Radiomics: The process and the challenges","volume":"30","author":"Kumar","year":"2012","journal-title":"Magnetic Resonance Imaging"},{"key":"10.1016\/j.bspc.2023.105543_b0100","first-page":"1642","article-title":"REGIST: right time to renovate?","volume":"43","author":"Burton","year":"2007","journal-title":"European Journal of Cancer"},{"key":"10.1016\/j.bspc.2023.105543_b0105","doi-asserted-by":"crossref","first-page":"3449","DOI":"10.1158\/1078-0432.CCR-07-0238","article-title":"Imaging tumor vascular heterogeneity and angiogenesis using dynamic contrast-enhanced magnetic resonance imaging","volume":"13","author":"Jackson","year":"2007","journal-title":"Clinical Cancer Research"},{"key":"10.1016\/j.bspc.2023.105543_b0110","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1002\/mrm.10496","article-title":"Textural analysis of contrast-enhanced MR images of the breast","volume":"50","author":"Gibbs","year":"2003","journal-title":"Magnetic Resonance in Medicine"},{"key":"10.1016\/j.bspc.2023.105543_b0115","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1148\/radiol.2383050167","article-title":"Pulmonary nodules: Estimation of malignancy at thin-section helical CT - Effect of computer-aided diagnosis on performance of radiologists","volume":"239","author":"Awai","year":"2006","journal-title":"Radiology"},{"key":"10.1016\/j.bspc.2023.105543_b0120","doi-asserted-by":"crossref","first-page":"10799","DOI":"10.1007\/s00521-020-05082-4","article-title":"Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm","volume":"33","author":"Lu","year":"2021","journal-title":"Neural Computing and Applications"},{"key":"10.1016\/j.bspc.2023.105543_b0125","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1002\/int.22686","article-title":"NAGNN: Classification of COVID-19 based on neighboring aware representation from deep graph neural network","volume":"37","author":"Lu","year":"2022","journal-title":"International Journal of Intelligence Systems"},{"key":"10.1016\/j.bspc.2023.105543_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.101859","article-title":"Deep learning in food category recognition","volume":"98","author":"Zhang","year":"2023","journal-title":"Inf. Fusion."},{"key":"10.1016\/j.bspc.2023.105543_b0135","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.isatra.2022.01.013","article-title":"Intelligent fault diagnosis of hydraulic piston pump based on deep learning and Bayesian optimization","volume":"129","author":"Tang","year":"2022","journal-title":"ISA Transactions"},{"key":"10.1016\/j.bspc.2023.105543_b0140","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.compmedimag.2007.02.002","article-title":"Computer-aided diagnosis in medical imaging: Historical review, current status and future potential","volume":"31","author":"Doi","year":"2007","journal-title":"Computerized Medical Imaging and Graphics"},{"key":"10.1016\/j.bspc.2023.105543_b0145","doi-asserted-by":"crossref","first-page":"2389","DOI":"10.1016\/j.patcog.2013.09.024","article-title":"Subclass Discriminant Analysis of morphological and textural features for HEp-2 staining pattern classification","volume":"47","author":"Di Cataldo","year":"2014","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.bspc.2023.105543_b0150","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1007\/s10278-012-9547-6","article-title":"Combination of radiological and gray level co-occurrence matrix textural features used to distinguish solitary pulmonary nodules by computed tomography","volume":"26","author":"Wu","year":"2013","journal-title":"Journal of Digital Imaging"},{"key":"10.1016\/j.bspc.2023.105543_b0155","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s11548-015-1245-7","article-title":"Automated pulmonary nodule CT image characterization in lung cancer screening","volume":"11","author":"Reeves","year":"2016","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"key":"10.1016\/j.bspc.2023.105543_b0160","first-page":"41","article-title":"Making large scale SVM learning practical","author":"Scholkopf","year":"1999","journal-title":"Adv. Kernel Methods Support Vector Learn."},{"key":"10.1016\/j.bspc.2023.105543_b0165","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks Editor","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Leaming."},{"key":"10.1016\/j.bspc.2023.105543_b0170","first-page":"376","author":"Laws","year":"1980","journal-title":"Rapid Texture Identification"},{"key":"10.1016\/j.bspc.2023.105543_b0175","doi-asserted-by":"crossref","unstructured":"S.K. Dilger, A. Judisch, J. Uthoff, E. Hammond, J.D. Newell, J.C. Sieren, Improved pulmonary nodule classification utilizing lung parenchyma texture features, In: Med. Imaging 2015 Comput. Diagnosis, SPIE, 2015: p. 94142T.","DOI":"10.1117\/12.2081397"},{"key":"10.1016\/j.bspc.2023.105543_b0180","first-page":"1155","article-title":"Lung nodule classification with multilevel patch-based context analysis","volume":"61","author":"Zhang","year":"2014","journal-title":"I.E.E.E. Transactions on Bio-Medical Engineering"},{"key":"10.1016\/j.bspc.2023.105543_b0185","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.jbi.2015.05.011","article-title":"A weighted rule based method for predicting malignancy of pulmonary nodules by nodule characteristics","volume":"56","author":"Kaya","year":"2015","journal-title":"Journal of Biomedical Informatics"},{"key":"10.1016\/j.bspc.2023.105543_b0190","doi-asserted-by":"crossref","unstructured":"S.G. Armato, G. McLennan, L. Bidaut, M.F. McNitt-Gray, C.R. Meyer, A.P. Reeves, B. Zhao, D.R. Aberle, C.I. Henschke, E.A. Hoffman, E.A. Kazerooni, H. MacMahon, E.J.R. Van Beek, D. Yankelevitz, A.M. Biancardi, P.H. Bland, M.S. Brown, R.M. Engelmann, G.E. Laderach, D. Max, R.C. Pais, D.P.Y. Qing, R.Y. Roberts, A.R. Smith, A. Starkey, P. Batra, P. Caligiuri, A. Farooqi, G.W. Gladish, C.M. Jude, R.F. Munden, I. Petkovska, L.E. Quint, L.H. Schwartz, B. Sundaram, L.E. Dodd, C. Fenimore, D. Gur, N. Petrick, J. Freymann, J. Kirby, B. Hughes, A. Vande Casteele, S. Gupte, M. Sallam, M.D. Heath, M.H. Kuhn, E. Dharaiya, R. Burns, D.S. Fryd, M. Salganicoff, V. Anand, U. Shreter, S. Vastagh, B.Y. Croft, L.P. Clarke, The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans, Med. Phys. 38 (2011) 915\u2013931.","DOI":"10.1118\/1.3528204"},{"key":"10.1016\/j.bspc.2023.105543_b0195","unstructured":"S. Balakrishnama, A. Ganapathiraju, Institute For Signal And Information Processing Linear Discriminant Analysis-A Brief Tutorial."},{"key":"10.1016\/j.bspc.2023.105543_b0200","unstructured":"I.T. Young, Introduction To Statistical Pattern Recognition., In: Proc Natl Electron Conf, 1974: pp. 349\u2013352."},{"key":"10.1016\/j.bspc.2023.105543_b0205","doi-asserted-by":"crossref","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","article-title":"ML-KNN: A lazy learning approach to multi-label learning","volume":"40","author":"Zhang","year":"2007","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.bspc.2023.105543_b0210","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"Journal of Computer and System Sciences"},{"issue":"45","key":"10.1016\/j.bspc.2023.105543_b0215","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"451","author":"Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"10.1016\/j.bspc.2023.105543_b0220","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1056\/NEJMoa1214726","article-title":"Probability of cancer in pulmonary nodules detected on first screening CT","volume":"369","author":"McWilliams","year":"2013","journal-title":"The New England Journal of Medicine"},{"key":"10.1016\/j.bspc.2023.105543_b0225","doi-asserted-by":"crossref","unstructured":"G.G. Lehmann, D. Legland, Efficient N-Dimensional surface estimation using Crofton formula and run-length encoding, (2012). https:\/\/hal.inrae.fr\/hal-02811118 (accessed September 20, 2022).","DOI":"10.54294\/wdu86d"},{"key":"10.1016\/j.bspc.2023.105543_b0230","doi-asserted-by":"crossref","unstructured":"P.H. Westfall, Kurtosis as Peakedness, 1905\u20132014. R.I.P. (2014) 191\u2013195.","DOI":"10.1080\/00031305.2014.917055"},{"key":"10.1016\/j.bspc.2023.105543_b0235","unstructured":"Measures of Skewness and Kurtosis. https:\/\/www.itl.nist.gov\/div898\/handbook\/eda\/section3\/eda35b.htm (accessed September 20, 2022)."},{"key":"10.1016\/j.bspc.2023.105543_b0240","series-title":"Probability and statistics for engineers and scientists","author":"Walpole","year":"1993"},{"key":"10.1016\/j.bspc.2023.105543_b0245","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/PROC.1979.11328","article-title":"Statistical and structural approaches to texture","volume":"67","author":"Haralick","year":"1979","journal-title":"Proceedings of the IEEE"},{"key":"10.1016\/j.bspc.2023.105543_b0250","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1109\/36.752194","article-title":"Texture analysis of sar sea ice imagery using gray level co-occurrence matrices","volume":"37","author":"Soh","year":"1999","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"10.1016\/j.bspc.2023.105543_b0255","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5589\/m02-004","article-title":"An analysis of co-occurrence texture statistics as a function of grey level quantization","volume":"28","author":"Clausi","year":"2002","journal-title":"Canadian Journal of Remote Sensing"},{"key":"10.1016\/j.bspc.2023.105543_b0260","first-page":"140","article-title":"Texture indexes and gray level size zone matrix","author":"Thibault","year":"2009","journal-title":"Appl. to Cell Nucl. Classif. PRIP."},{"key":"10.1016\/j.bspc.2023.105543_b0265","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/S0146-664X(75)80008-6","article-title":"Texture analysis using gray level run lengths","volume":"4","author":"Galloway","year":"1975","journal-title":"Comput. Graph. Image Process."},{"key":"10.1016\/j.bspc.2023.105543_b0270","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/0167-8655(91)80014-2","article-title":"Image characterizations based on joint gray level\u2014run length distributions","volume":"12","author":"Dasarathy","year":"1991","journal-title":"Pattern Recognition Letters"},{"key":"10.1016\/j.bspc.2023.105543_b0275","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/0167-8655(90)90112-F","article-title":"Use of gray value distribution of run lengths for texture analysis","volume":"11","author":"Chu","year":"1990","journal-title":"Pattern Recognition Letters"},{"key":"10.1016\/j.bspc.2023.105543_b0280","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1109\/21.44046","article-title":"Texural features corresponding to texural properties","volume":"19","author":"Amadasun","year":"1989","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics"},{"key":"10.1016\/j.bspc.2023.105543_b0285","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/21.97458","article-title":"A survey of decision tree classifier methodology","volume":"21","author":"Safavian","year":"1991","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics"},{"key":"10.1016\/j.bspc.2023.105543_b0290","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"10.1016\/j.bspc.2023.105543_b0295","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1162\/089976601300014493","article-title":"Improvements to Platt\u2019s SMO algorithm for SVM classifier design","volume":"13","author":"Keerthi","year":"2001","journal-title":"Neural Computation"},{"key":"10.1016\/j.bspc.2023.105543_b0300","series-title":"The nature of statistical learning theory","author":"Vapnik","year":"1999"},{"key":"10.1016\/j.bspc.2023.105543_b0305","first-page":"986","article-title":"KNN model-based approach in classification","volume":"2888","author":"Guo","year":"2003","journal-title":"Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics)."},{"key":"10.1016\/j.bspc.2023.105543_b0310","doi-asserted-by":"crossref","unstructured":"D.J. Hand, Principles of data mining, in: Drug Saf., Springer, 2007: pp. 621\u2013622.","DOI":"10.2165\/00002018-200730070-00010"},{"key":"10.1016\/j.bspc.2023.105543_b0315","unstructured":"P. Langley, W. Iba, K. Thompson, An Analysis of Bayesian Classiiers, (1992)."},{"key":"10.1016\/j.bspc.2023.105543_b0320","first-page":"103","volume":"29","author":"Domingos","year":"1997","journal-title":"On the Optimality of the Simple Bayesian Classifier under Zero-One Loss"},{"key":"10.1016\/j.bspc.2023.105543_b0325","series-title":"Linear Discriminant Analysis","first-page":"27","author":"Xanthopoulos","year":"2013"},{"key":"10.1016\/j.bspc.2023.105543_b0330","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","article-title":"The use of multiple measurements in taxonomic problems","volume":"7","author":"Fisher","year":"1936","journal-title":"Ann. Eugen."},{"key":"10.1016\/j.bspc.2023.105543_b0335","unstructured":"Y. Freund, R.E. Schapire, others, Experiments with a new boosting algorithm, in: Icml, 1996: pp. 148\u2013156."},{"key":"10.1016\/j.bspc.2023.105543_b0340","doi-asserted-by":"crossref","DOI":"10.1155\/2019\/2717454","article-title":"Breast microcalcification diagnosis using deep convolutional neural network from digital mammograms","volume":"2019","author":"Cai","year":"2019","journal-title":"Comput. Math. Methods Med."},{"key":"10.1016\/j.bspc.2023.105543_b0345","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMI.3.4.044506","article-title":"LUNGx Challenge for computerized lung nodule classification","volume":"3","author":"Armato","year":"2016","journal-title":"J. Med. Imaging."},{"key":"10.1016\/j.bspc.2023.105543_b0350","doi-asserted-by":"crossref","unstructured":"H. M, S. M.N, A Review on Evaluation Metrics for Data Classification Evaluations, Int. J. Data Min. Knowl. Manag. Process. 5 (2015) 01\u201311.","DOI":"10.5121\/ijdkp.2015.5201"},{"key":"10.1016\/j.bspc.2023.105543_b0355","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1109\/TKDE.2005.50","article-title":"Using AUC and accuracy in evaluating learning algorithms","volume":"17","author":"Huang","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"45","key":"10.1016\/j.bspc.2023.105543_b0360","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1023\/A:1010920819831","article-title":"A simple generalisation of the area under the ROC curve for multiple class classification problems","volume":"452","author":"Hand","year":"2001","journal-title":"Mach. Learn."},{"key":"10.1016\/j.bspc.2023.105543_b0365","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/s10278-020-00417-y","article-title":"Lung nodule classification using biomarkers, volumetric radiomics, and 3D CNNs","volume":"34","author":"Mehta","year":"2021","journal-title":"J. Digit. Imaging."},{"key":"10.1016\/j.bspc.2023.105543_b0370","doi-asserted-by":"crossref","unstructured":"S.G. Armato, G. McLennan, L. Bidaut, M.F. McNitt-Gray, C.R. Meyer, A.P. Reeves, B. Zhao, D.R. Aberle, C.I. Henschke, E.A. Hoffman, E.A. Kazerooni, H. MacMahon, E.J.R. van Beek, D. Yankelevitz, A.M. Biancardi, P.H. Bland, M.S. Brown, R.M. Engelmann, G.E. Laderach, D. Max, R.C. Pais, D.P.-Y. Qing, R.Y. Roberts, A.R. Smith, A. Starkey, P. Batra, P. Caligiuri, A. Farooqi, G.W. Gladish, C.M. Jude, R.F. Munden, I. Petkovska, L.E. Quint, L.H. Schwartz, B. Sundaram, L.E. Dodd, C. Fenimore, D. Gur, N. Petrick, J. Freymann, J. Kirby, B. Hughes, A. Vande Casteele, S. Gupte, M. Sallam, M.D. Heath, M.H. Kuhn, E. Dharaiya, R. Burns, D.S. Fryd, M. Salganicoff, V. Anand, U. Shreter, S. Vastagh, B.Y. Croft, L.P. Clarke, The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans, Med. Phys. 38 (2011) 915\u2013931.","DOI":"10.1118\/1.3528204"},{"key":"10.1016\/j.bspc.2023.105543_b0375","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1002\/jemt.23326","article-title":"Automated lung nodule detection and classification based on multiple classifiers voting","volume":"82","author":"Saba","year":"2019","journal-title":"Microsc. Res. Tech."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S174680942300976X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S174680942300976X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T07:05:54Z","timestamp":1700031954000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S174680942300976X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":75,"alternative-id":["S174680942300976X"],"URL":"http:\/\/dx.doi.org\/10.1016\/j.bspc.2023.105543","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2024,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Texture and Radiomics inspired Data-Driven Cancerous Lung Nodules Severity Classification","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2023.105543","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"105543"}}
