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In the experiments, the authors implement the DBT-GBM model into real-time data, the samples of three anatomic sections of a T1w 3D MRI (axial, sagittal and coronal cross-sections) on the GBM-3D-Slicer datasets and the CBTC datasets. The implementation results show that the proposed DBT-GBM robustly detects the GBM disease patterns and cancer nuclei (involving the omics indicative of brain tumors pathologically) in medical imaging, leading to improved segmentation performance in comparison.<\/jats:p>","DOI":"10.4018\/ijssci.2017100103","type":"journal-article","created":{"date-parts":[[2018,1,23]],"date-time":"2018-01-23T12:49:05Z","timestamp":1516711745000},"page":"34-45","source":"Crossref","is-referenced-by-count":3,"title":["An Innovative Model for Detecting Brain Tumors and Glioblastoma Multiforme Disease Patterns"],"prefix":"10.4018","volume":"9","author":[{"given":"Peifang","family":"Guo","sequence":"first","affiliation":[{"name":"Independant Researcher, Montreal, Canada"}]},{"given":"Prabir","family":"Bhattacharya","sequence":"additional","affiliation":[{"name":"Morgan State University, Baltimore, MD, USA"}]}],"member":"2432","reference":[{"key":"IJSSCI.2017100103-0","doi-asserted-by":"publisher","DOI":"10.4018\/IJSSCI.2015040102"},{"key":"IJSSCI.2017100103-1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2003.10.012"},{"key":"IJSSCI.2017100103-2","doi-asserted-by":"publisher","DOI":"10.7937\/K9\/TCIA.2017.KLXWJJ1Q"},{"key":"IJSSCI.2017100103-3","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.117"},{"key":"IJSSCI.2017100103-4","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/58\/13\/R97"},{"key":"IJSSCI.2017100103-5","unstructured":"Computational Brain Tumor Cluster of Event (CBTC). 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