{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:19:11Z","timestamp":1774966751420,"version":"3.50.1"},"reference-count":29,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Segmentation of brain image should be done accurately as it can help to predict deadly brain tumor disease so that it can be possible to control the malicious segments of brain image if known beforehand. The accuracy of the brain tumor analysis can be enhanced through the brain tumor segmentation procedure. Earlier DCNN models do not consider the weights as of learning instances which may decrease accuracy levels of the segmentation procedure. Considering the above point, we have suggested a framework for optimizing the network parameters such as weight and bias vector of DCNN models using swarm intelligent based algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). The simulation results reveals that the WOA optimized DCNN segmentation model is outperformed than other three optimization based DCNN models <jats:italic>i.e.<\/jats:italic>, GA-DCNN, PSO-DCNN, GWO-DCNN.<\/jats:p>","DOI":"10.1515\/comp-2020-0166","type":"journal-article","created":{"date-parts":[[2021,6,6]],"date-time":"2021-06-06T20:05:09Z","timestamp":1623009909000},"page":"380-390","source":"Crossref","is-referenced-by-count":19,"title":["Segmentation of MRI Brain Tumor Image using Optimization based Deep Convolutional Neural networks (DCNN)"],"prefix":"10.1515","volume":"11","author":[{"given":"Pradipta Kumar","family":"Mishra","sequence":"first","affiliation":[{"name":"School of Computer Engineering , Kalinga Institute of Industrial Technology (Deemed to be) University , Bhubaneswar , Odisha , India"}]},{"given":"Suresh Chandra","family":"Satapathy","sequence":"additional","affiliation":[{"name":"School of Computer Engineering , Kalinga Institute of Industrial Technology (Deemed to be) University , Bhubaneswar , Odisha , India"}]},{"given":"Minakhi","family":"Rout","sequence":"additional","affiliation":[{"name":"School of Computer Engineering , Kalinga Institute of Industrial Technology (Deemed to be) University , Bhubaneswar , Odisha , India"}]}],"member":"374","published-online":{"date-parts":[[2021,6,5]]},"reference":[{"key":"2022020121510254343_j_comp-2020-0166_ref_001","doi-asserted-by":"crossref","unstructured":"A. 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Alves, and C. A. Silva, \u201cBrain tumor segmentation using convolutional neural networks in MRI images,\u201d IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1240\u20131251, 2016. https:\/\/doi.org\/10.1109\/TMI.2016.2538465.","DOI":"10.1109\/TMI.2016.2538465"},{"key":"2022020121510254343_j_comp-2020-0166_ref_013","doi-asserted-by":"crossref","unstructured":"B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al., \u201cThe multimodal brain tumor image segmentation benchmark (BRATS),\u201d IEEE Trans. Med. Imaging, vol. 34, no. 10, pp. 1993\u20132024, December 4 2014. https:\/\/doi.org\/10.1109\/TMI.2014.2377694.","DOI":"10.1109\/TMI.2014.2377694"},{"key":"2022020121510254343_j_comp-2020-0166_ref_014","doi-asserted-by":"crossref","unstructured":"V. Rajinikanth, S. L. Fernandes, B. Bhushan, and N. R. Sunder, Segmentation and analysis of brain tumor using Tsallis entropy and regularised level set. 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