{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T08:01:36Z","timestamp":1771747296096,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2013,7,9]],"date-time":"2013-07-09T00:00:00Z","timestamp":1373328000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2014,2]]},"DOI":"10.1007\/s10278-013-9620-9","type":"journal-article","created":{"date-parts":[[2013,7,8]],"date-time":"2013-07-08T13:49:14Z","timestamp":1373291354000},"page":"90-97","source":"Crossref","is-referenced-by-count":21,"title":["Support Vector Machine Model for Diagnosing Pneumoconiosis Based on Wavelet Texture Features of Digital Chest Radiographs"],"prefix":"10.1007","volume":"27","author":[{"given":"Biyun","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Hui","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Budong","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Kuan","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2013,7,9]]},"reference":[{"key":"9620_CR1","unstructured":"International Labor Organization (ILO): Guidelines for the use of the ILO international classification of radiographs of pneumoconiosis. Occupational Safety and Health Series, No. 22 (Rev.). International Labor Office, Geneva Switzerland, 1980."},{"key":"9620_CR2","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1109\/TPAMI.1980.6592371","volume":"2","author":"AM Savol","year":"1980","unstructured":"Savol AM, Li CC, Hoy RJ: Computer-aided recognition of small rounded pneumoconiosis opacities in chest X-rays. IEEE Trans Pattern Anal Mach Intell 2:479\u2013482, 1980","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9620_CR3","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1109\/TBME.1975.324475","volume":"22","author":"EL Hall","year":"1975","unstructured":"Hall EL, Crawford WO, Roberts FE: Computer classification of pneumoconiosis from radiographs of coal workers. IEEE Trans Biomed Eng 22:518\u2013527, 1975","journal-title":"IEEE Trans Biomed Eng"},{"key":"9620_CR4","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1007\/s10278-010-9276-7","volume":"24","author":"P Yu","year":"2011","unstructured":"Yu P, Xu H, Zhu Y, Yang C, Sun X, Zhao J, et al: An automatic computer-aided detection scheme for pneumoconiosis on digital chest radiographs. J Digit Imaging 24:382\u2013393, 2011","journal-title":"J Digit Imaging"},{"key":"9620_CR5","unstructured":"Xu H, Tao X, Sundararajan R, et al.: Computer aided detection for pneumoconiosis screening on digital chest radiographs. Proc. Third International Workshop on Pulmonary Image Analysis, 129\u2013138, 2010."},{"key":"9620_CR6","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.1007\/s10278-010-9357-7","volume":"24","author":"E Okumura","year":"2011","unstructured":"Okumura E, Kawashita I, Ishida T: Computerized analysis of pneumoconiosis in digital chest radiography: effect of artificial neural network trained with power spectra. J Digit Imaging 24:1126\u20131132, 2011","journal-title":"J Digit Imaging"},{"key":"9620_CR7","doi-asserted-by":"crossref","unstructured":"Cai C, Zhu B, Chen H: Computer-aided diagnosis for pneumoconiosis based on texture analysis on digital chest radiographs. Proceedings of International Conference on Electronic, Communication and Computer Science. Guilin, China, 2012 June 15\u201317.","DOI":"10.4028\/www.scientific.net\/AMM.241-244.244"},{"key":"9620_CR8","doi-asserted-by":"crossref","first-page":"11503","DOI":"10.1016\/j.eswa.2012.04.001","volume":"39","author":"H Chen","year":"2012","unstructured":"Chen H, Zhang J, Xu Y, Chen B, Zhang K: Performance comparison of artificial neural network and logistic regression model for differentiating lung nodules on CT scans. Expert Syst Appl 39:11503\u201311509, 2012","journal-title":"Expert Syst Appl"},{"key":"9620_CR9","doi-asserted-by":"crossref","unstructured":"Zhu B, Chen H: Morphological reconstruction based segmentation of lung fields on digital radiographs. Proceedings of International Conference on Electronic, Communication and Computer Science. Guilin, China, 2012 June 15\u201317.","DOI":"10.4028\/www.scientific.net\/AMR.605-607.2155"},{"key":"9620_CR10","doi-asserted-by":"crossref","first-page":"3197","DOI":"10.1016\/j.patrec.2003.08.005","volume":"24","author":"S Arivazhagan","year":"2003","unstructured":"Arivazhagan S, Ganesan L: Texture segmentation using wavelet transform. Pattern Recogn Lett 24:3197\u20133203, 2003","journal-title":"Pattern Recogn Lett"},{"key":"9620_CR11","unstructured":"Kocio\u0142ek M, Materka A, Strzelecki M, Szczypi\u0144ski P: Discrete wavelet transform-derived features for digital image texture analysis. Proceedings of International Conference on Signals and Electronic Systems. Lodz, Poland, 2001 September 18\u201321."},{"key":"9620_CR12","first-page":"81","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan JR: Induction decision tree. Mach Learn 1:81\u2013106, 1986","journal-title":"Mach Learn"},{"key":"9620_CR13","first-page":"851","volume":"125","author":"C Li","year":"2012","unstructured":"Li C, Zhi X, Ma J, Cui Z, Zhu Z, Zhang C, et al: Performance comparison between logistic regression, decision trees, and multilayer perceptron in predicting peripheral neuropathy in type 2 diabetes mellitus. Chin Med J (Engl) 125:851\u2013857, 2012","journal-title":"Chin Med J (Engl)"},{"key":"9620_CR14","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-09823-4","volume-title":"Data Mining and Knowledge Discovery Handbook","author":"O Maimon","year":"2010","unstructured":"Maimon O, Rokach L: Data Mining and Knowledge Discovery Handbook, 2nd edition. Springer, New York, 2010","edition":"2"},{"key":"9620_CR15","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s10278-009-9185-9","volume":"23","author":"Y Zhu","year":"2010","unstructured":"Zhu Y, Tan Y, Hua Y, Wang M, Zhang G, Zhang J: Feature selection and performance evaluation of support vector machine (SVM)-based classifier for differentiating benign and malignant pulmonary nodules by computed tomography. J Digit Imaging 23:51\u201365, 2010","journal-title":"J Digit Imaging"},{"key":"9620_CR16","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511809682","volume-title":"Kernel Methods for Pattern Analysis","author":"J Shawe-Taylor","year":"2004","unstructured":"Shawe-Taylor J, Cristianini N: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge, 2004"},{"key":"9620_CR17","volume-title":"Advanced Data Mining Techniques","author":"DL Olson","year":"2008","unstructured":"Olson DL, Delen D: Advanced Data Mining Techniques. Springer, LLC, Berlin, 2008"},{"key":"9620_CR18","doi-asserted-by":"crossref","unstructured":"Kondo H, Zhao B, Mino M: Automated quantitative analysis for pneumoconiosis. Proceedings of International Symposium on Multispectral Image Processing. Wuhan, China, 1998 Oct 21\u201323.","DOI":"10.1117\/12.323592"},{"key":"9620_CR19","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1002\/scj.4690211205","volume":"21","author":"X Chen","year":"1990","unstructured":"Chen X, Toriwaki J, Hasegawa J: Automated classification of pneumoconiosis radiographs based on recognition of small rounded opacities. Syst Comput Jpn 21:33\u201344, 1990","journal-title":"Syst Comput Jpn"},{"key":"9620_CR20","doi-asserted-by":"crossref","unstructured":"Murray V, Pattichis MS, Davis H, Barriga ES, Soliz P: Multiscale AM-FM analysis of pneumoconiosis x-ray images. Proceedings of IEEE International Conference on Image Processing. Kochi, India, 2009 Nov 7\u201310.","DOI":"10.1109\/ICIP.2009.5414522"},{"key":"9620_CR21","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.artmed.2004.07.002","volume":"34","author":"D Delen","year":"2005","unstructured":"Delen D, Walker G, Kadam A: Predicting breast cancer survivability: a comparison of three data mining methods. Artif Intell Med 34:113\u2013127, 2005","journal-title":"Artif Intell Med"},{"key":"9620_CR22","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.acra.2009.01.029","volume":"16","author":"CE McLaren","year":"2009","unstructured":"McLaren CE, Chen WP, Nie K, Su MY: Prediction of malignant breast lesions from MRI features: a comparison of artificial neural network and logistic regression techniques. Acad Radiol 16:842\u2013851, 2009","journal-title":"Acad Radiol"},{"key":"9620_CR23","first-page":"1485","volume":"3","author":"MM Mohamed","year":"2003","unstructured":"Mohamed MM, Abdel-Galil TK, Salama MA, EI-Saadany EF, Kamel M, Fenster A, Downey DB, Rizkalla K: Prostate cancer diagnosis based on Gabor filter texture segmentation of ultrasound image. Proc IEEE Can Conf Electr Comput Eng 3:1485\u20131488, 2003","journal-title":"Proc IEEE Can Conf Electr Comput Eng"},{"key":"9620_CR24","doi-asserted-by":"crossref","first-page":"10018","DOI":"10.1016\/j.eswa.2011.02.016","volume":"38","author":"B B\u00e1rbara","year":"2011","unstructured":"B\u00e1rbara B, Pineda-Bautista JA, Carrasco-Ochoa J: Fco Mart\u00ednez-Trinidad: General framework for class-specific feature selection. Expert Syst Appl 38:10018\u201310024, 2011","journal-title":"Expert Syst Appl"},{"key":"9620_CR25","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning","author":"T Hastie","year":"2009","unstructured":"Hastie T, Tibshirani R, Friedman J: The Elements of Statistical Learning, 2nd edition. Springer, New York, 2009","edition":"2"},{"key":"9620_CR26","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1023\/A:1007608224229","volume":"40","author":"T Lim","year":"2000","unstructured":"Lim T, Loh W, Shih Y: A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Mach Learn 40:203\u2013228, 2000","journal-title":"Mach Learn"},{"key":"9620_CR27","doi-asserted-by":"crossref","first-page":"15202","DOI":"10.1016\/j.eswa.2011.05.081","volume":"38","author":"M Elangovan","year":"2011","unstructured":"Elangovan M, Sugumaran V, Ramachandran KI, Ravikumar S: Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool. Expert Syst Appl 38:15202\u201315207, 2011","journal-title":"Expert Syst Appl"},{"key":"9620_CR28","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.atmosres.2012.02.007","volume":"109","author":"T Islam","year":"2012","unstructured":"Islam T, Rico-Ramirez MA, Han D, Srivastava PK: Artificial intelligence techniques for clutter identification with polarimetric radar signatures. Atmos Res 109:95\u2013113, 2012","journal-title":"Atmos Res"},{"key":"9620_CR29","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.enggeo.2011.09.006","volume":"123","author":"M Marjanovic","year":"2011","unstructured":"Marjanovic M, Kovacevic M, Bajat B, Vozenilek V: Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123:225\u2013234, 2011","journal-title":"Eng Geol"},{"key":"9620_CR30","volume-title":"Data Mining: Concepts and Techniques","author":"J Han","year":"2006","unstructured":"Han J, Kamber M: Data Mining: Concepts and Techniques, 2nd edition. Elsevier, Maryland Heights, 2006","edition":"2"},{"key":"9620_CR31","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1118\/1.3284974","volume":"37","author":"TW Way","year":"2010","unstructured":"Way TW, Sahiner B, Hadjiiski LM, Chan HP: Effect of finite sample size on feature selection and classification: a simulation study. Med Phys 37:907\u2013920, 2010","journal-title":"Med Phys"},{"key":"9620_CR32","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1118\/1.2868757","volume":"35","author":"B Sahiner","year":"2008","unstructured":"Sahiner B, Chan HP, Hadjiiski L: Classifier performance prediction for computer-aided diagnosis using a limited dataset. Med Phys 35:1559\u20131570, 2008","journal-title":"Med Phys"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-013-9620-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10278-013-9620-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-013-9620-9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T12:52:02Z","timestamp":1563367922000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10278-013-9620-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,7,9]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2014,2]]}},"alternative-id":["9620"],"URL":"https:\/\/doi.org\/10.1007\/s10278-013-9620-9","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,7,9]]}}}