{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T10:30:52Z","timestamp":1777285852772,"version":"3.51.4"},"reference-count":37,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Glioma is a type of fast-growing brain tumor in which the shape, size, and location of the tumor vary from patient to patient. Manual extraction of a region of interest (tumor) with the help of a radiologist is a very difficult and time-consuming task. To overcome this problem, we proposed a fully automated deep learning-based ensemble method of brain tumor segmentation on four different 3D multimodal magnetic resonance imaging (MRI) scans. The segmentation is performed by three most efficient encoder\u2013decoder deep models for segmentation and their results are measured through the well-known segmentation metrics. Then, a statistical analysis of the models was performed and an ensemble model is designed by considering the highest Matthews correlation coefficient using a particular MRI modality. There are two main contributions of the article: first the detailed comparison of the three models, and second proposing an ensemble model by combining the three models based on their segmentation accuracy. The model is evaluated using the brain tumor segmentation (BraTS) 2017 dataset and the <jats:italic>F<\/jats:italic>1 score of the final combined model is found to be 0.92, 0.95, 0.93, and 0.84 for whole tumor, core, enhancing tumor, and edema sub-tumor, respectively. Experimental results show that the model outperforms the state of the art.<\/jats:p>","DOI":"10.1515\/comp-2022-0242","type":"journal-article","created":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T13:33:01Z","timestamp":1653917581000},"page":"211-226","source":"Crossref","is-referenced-by-count":29,"title":["Deep learning-based ensemble model for brain tumor segmentation using multi-parametric MR scans"],"prefix":"10.1515","volume":"12","author":[{"given":"Suchismita","family":"Das","sequence":"first","affiliation":[{"name":"Computer Science & Engineering, IIIT Bhubaneswar , Bhubaneswar , Odisha, 751003 , India"},{"name":"KIIT University , Odisha , 751024 , India"}]},{"given":"Srijib","family":"Bose","sequence":"additional","affiliation":[{"name":"Computer Science & Engineering, KIIT University , Odisha , 751024 , India"}]},{"given":"Gopal Krishna","family":"Nayak","sequence":"additional","affiliation":[{"name":"Computer Science & Engineering, IIIT Bhubaneswar , Bhubaneswar , Odisha, 751003 , India"}]},{"given":"Sanjay","family":"Saxena","sequence":"additional","affiliation":[{"name":"Computer Science & Engineering, IIIT Bhubaneswar , Bhubaneswar , Odisha, 751003 , India"}]}],"member":"374","published-online":{"date-parts":[[2022,5,30]]},"reference":[{"key":"2022081707553232687_j_comp-2022-0242_ref_001","unstructured":"K. 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Experience, p. e6501."},{"key":"2022081707553232687_j_comp-2022-0242_ref_006","doi-asserted-by":"crossref","unstructured":"S. Das, \u201cBrain Tumor Segmentation from MRI Images Using Deep Learning Framework,\u201d in: Progress in Computing, Analytics and Networking, Springer, Singapore, 2020, pp. 105\u2013114.","DOI":"10.1007\/978-981-15-2414-1_11"},{"key":"2022081707553232687_j_comp-2022-0242_ref_007","doi-asserted-by":"crossref","unstructured":"S. Das, G. Nayak, S. Saxena, S. C. Satpathy, \u201cEffect of learning parameters on the performance of U-Net Model in segmentation of Brain tumor,\u201d Multimed. Tools Appl., pp. 1\u201319, 2021.","DOI":"10.1007\/s11042-021-11273-5"},{"key":"2022081707553232687_j_comp-2022-0242_ref_008","doi-asserted-by":"crossref","unstructured":"S. Saxena, P. Mohapatra, and S. 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