{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T13:21:53Z","timestamp":1760016113301},"reference-count":16,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2012,4,16]],"date-time":"2012-04-16T00:00:00Z","timestamp":1334534400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Numer Methods Biomed Eng"],"published-print":{"date-parts":[[2012,11]]},"abstract":"<jats:title>SUMMARY<\/jats:title><jats:p>The present study is conducted to develop an interactive computer aided diagnosis (CAD) system for assisting radiologists in multiclass classification of brain tumors. In this paper, primary brain tumors such as astrocytoma, glioblastoma multiforme, childhood tumor\u2010medulloblastoma, meningioma and secondary tumor\u2010metastases along with normal regions are classified by a dual level neural network ensemble. Two hundred eighteen texture and intensity features are extracted from 856 segmented regions of interest (SROIs) and are taken as input. PCA is used for reduction of dimensionality of the feature space. The study is performed on a diversified dataset of 428 post contrast T1\u2010weighted magnetic resonance images of 55 patients. Two sets of experiments are performed. In the first experiment, random selection is used which may allow SROIs from the same patient having similar characteristics to appear in both training and testing simultaneously. In the second experiment, not even a single SROI from the same patient is common during training and testing. In the first experiment, it is observed that the dual level neural network ensemble has enhanced the overall accuracy to 95.85% compared with 91.97% of single level artificial neural network. The proposed method delivers high accuracy for each class. The accuracy obtained for each class is: astrocytoma 96.29%, glioblastoma multiforme 96.15%, childhood tumor\u2010medulloblastoma 90%, meningioma 93.00%, secondary tumor\u2010metastases 96.67% and normal regions 97.41%. This study reveals that dual level neural network ensemble provides better results than the single level artificial neural network. In the second experiment, overall classification accuracy of 90.4% was achieved. The generalization ability of this approach can be tested by analyzing larger datasets. The extensive training will also further improve the performance of the proposed dual network ensemble. Quantitative results obtained from the proposed method will assist the radiologist in forming a better decision for classifying brain tumors. Copyright \u00a9 2012 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/cnm.2481","type":"journal-article","created":{"date-parts":[[2012,4,16]],"date-time":"2012-04-16T11:38:51Z","timestamp":1334576331000},"page":"1107-1120","source":"Crossref","is-referenced-by-count":21,"title":["A dual neural network ensemble approach for multiclass brain tumor classification"],"prefix":"10.1002","volume":"28","author":[{"given":"Jainy","family":"Sachdeva","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering Indian Institute of Technology Roorkee  Roorkee India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vinod","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Indian Institute of Technology Roorkee  Roorkee India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Indra","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Indian Institute of Technology Roorkee  Roorkee India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niranjan","family":"Khandelwal","sequence":"additional","affiliation":[{"name":"Department of Radio Diagnosis Post Graduate Institute of Medical Education and Research  Chandigarh India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chirag kamal","family":"Ahuja","sequence":"additional","affiliation":[{"name":"Department of Radio Diagnosis Post Graduate Institute of Medical Education and Research  Chandigarh India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2012,4,16]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2008.03.002"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmr.2004.06.010"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmr.2004.12.007"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2007.02.002"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2007.10.007"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74484-9_21"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22147"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1080\/1931308X.2007.10644134"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2009.07.002"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1973.4309314"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(02)00056-9"},{"key":"e_1_2_8_13_1","unstructured":"ZhangJ TanT MaL.Invariant texture segmentation via circular Gabor filters.Proceedings of the 16th International Conference on Pattern Recognition Quebec City QC Canada 2002."},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2002.1017623"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.102102699"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.824819"},{"key":"e_1_2_8_17_1","volume-title":"Pattern Classification","author":"Duda RO","year":"2001"}],"container-title":["International Journal for Numerical Methods in Biomedical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fcnm.2481","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cnm.2481","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T15:57:12Z","timestamp":1697471832000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cnm.2481"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,4,16]]},"references-count":16,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2012,11]]}},"alternative-id":["10.1002\/cnm.2481"],"URL":"https:\/\/doi.org\/10.1002\/cnm.2481","archive":["Portico"],"relation":{},"ISSN":["2040-7939","2040-7947"],"issn-type":[{"value":"2040-7939","type":"print"},{"value":"2040-7947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,4,16]]}}}