{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T16:42:53Z","timestamp":1776357773993,"version":"3.51.2"},"reference-count":39,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Artificial Intelligence (AI) and computer-aided diagnosis (CAD) have revolutionised various aspects of modern life, particularly in the medical domain. These technologies enable efficient solutions for complex challenges, such as accurately segmenting brain tumour regions, which significantly aid medical professionals in monitoring and treating patients. This research focuses on segmenting glioma brain tumour lesions in MRI images by analysing them at the pixel level. The aim is to develop a deep learning-based approach that enables ensemble learning to achieve precise and consistent segmentation of brain tumours. While many studies have explored ensemble learning techniques in this area, most rely on aggregation functions like the Weighted Arithmetic Mean (WAM) without accounting for the interdependencies between classifier subsets. To address this limitation, the Choquet integral is employed for ensemble learning, along with a novel evaluation framework for fuzzy measures. This framework integrates coalition game theory, information theory, and Lambda fuzzy approximation. Three distinct fuzzy measure sets are computed using different weighting strategies informed by these theories. Based on these measures, three Choquet integrals are calculated for segmenting different components of brain lesions, and their outputs are subsequently combined. The BraTS-2020 online validation dataset is used to validate the proposed approach. Results demonstrate superior performance compared with several recent methods, achieving Dice Similarity Coefficients of 0.896, 0.851, and 0.792 and 95% Hausdorff distances of 5.96 mm, 6.65 mm, and 20.74 mm for the whole tumour, tumour core, and enhancing tumour core, respectively.<\/jats:p>","DOI":"10.3390\/info16070615","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T14:13:43Z","timestamp":1752761623000},"page":"615","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Brain Tumour Segmentation Using Choquet Integrals and Coalition Game"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6622-4355","authenticated-orcid":false,"given":"Makhlouf","family":"Derdour","sequence":"first","affiliation":[{"name":"Artificial Intelligence and Autonomous Things Laboratory, Larbi Ben M\u2019hidi University of Oum El Bouaghi, Oum el Bouaghi 04000, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed El Bachir","family":"Yahiaoui","sequence":"additional","affiliation":[{"name":"Laboratory of Mathematics, Informatics and Systems, Larbi Tebessi University of Tebessa, Tebessa 12022, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8645-6062","authenticated-orcid":false,"given":"Moustafa Sadek","family":"Kahil","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Autonomous Things Laboratory, Larbi Ben M\u2019hidi University of Oum El Bouaghi, Oum el Bouaghi 04000, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5872-9142","authenticated-orcid":false,"given":"Mohamed","family":"Gasmi","sequence":"additional","affiliation":[{"name":"Laboratory of Mathematics, Informatics and Systems, Larbi Tebessi University of Tebessa, Tebessa 12022, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7067-7848","authenticated-orcid":false,"given":"Mohamed Chahine","family":"Ghanem","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Liverpool, Liverpool L69 7ZX, UK"},{"name":"School of Computing and Digital Media, London Metropolitan University, London N7 8DB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yahiaoui, M.E., Derdour, M., Abdulghafor, R., Turaev, S., Gasmi, M., Bennour, A., Aborujilah, A., and Sarem, M.A. (2024). 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[1st ed.].","DOI":"10.1007\/978-3-030-15305-2"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/7\/615\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:11:38Z","timestamp":1760033498000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/7\/615"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,17]]},"references-count":39,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["info16070615"],"URL":"https:\/\/doi.org\/10.3390\/info16070615","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,17]]}}}