Brain Tumour Segmentation Using Choquet Integrals and Coalition Game
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 me...
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MDPI AG
2025-07-01
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| author | Makhlouf Derdour Mohammed El Bachir Yahiaoui Moustafa Sadek Kahil Mohamed Gasmi Mohamed Chahine Ghanem |
| author_facet | Makhlouf Derdour Mohammed El Bachir Yahiaoui Moustafa Sadek Kahil Mohamed Gasmi Mohamed Chahine Ghanem |
| author_sort | Makhlouf Derdour |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ca5ffa3b0e1a44939c5dfc35dcb40326 |
| institution | DOAJ |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-ca5ffa3b0e1a44939c5dfc35dcb403262025-08-20T02:45:52ZengMDPI AGInformation2078-24892025-07-0116761510.3390/info16070615Brain Tumour Segmentation Using Choquet Integrals and Coalition GameMakhlouf Derdour0Mohammed El Bachir Yahiaoui1Moustafa Sadek Kahil2Mohamed Gasmi3Mohamed Chahine Ghanem4Artificial Intelligence and Autonomous Things Laboratory, Larbi Ben M’hidi University of Oum El Bouaghi, Oum el Bouaghi 04000, AlgeriaLaboratory of Mathematics, Informatics and Systems, Larbi Tebessi University of Tebessa, Tebessa 12022, AlgeriaArtificial Intelligence and Autonomous Things Laboratory, Larbi Ben M’hidi University of Oum El Bouaghi, Oum el Bouaghi 04000, AlgeriaLaboratory of Mathematics, Informatics and Systems, Larbi Tebessi University of Tebessa, Tebessa 12022, AlgeriaDepartment of Computer Science, University of Liverpool, Liverpool L69 7ZX, UKArtificial 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.https://www.mdpi.com/2078-2489/16/7/615brain cancerMRIChoquet integralcoalition game theorydeep learningmedical imaging |
| spellingShingle | Makhlouf Derdour Mohammed El Bachir Yahiaoui Moustafa Sadek Kahil Mohamed Gasmi Mohamed Chahine Ghanem Brain Tumour Segmentation Using Choquet Integrals and Coalition Game Information brain cancer MRI Choquet integral coalition game theory deep learning medical imaging |
| title | Brain Tumour Segmentation Using Choquet Integrals and Coalition Game |
| title_full | Brain Tumour Segmentation Using Choquet Integrals and Coalition Game |
| title_fullStr | Brain Tumour Segmentation Using Choquet Integrals and Coalition Game |
| title_full_unstemmed | Brain Tumour Segmentation Using Choquet Integrals and Coalition Game |
| title_short | Brain Tumour Segmentation Using Choquet Integrals and Coalition Game |
| title_sort | brain tumour segmentation using choquet integrals and coalition game |
| topic | brain cancer MRI Choquet integral coalition game theory deep learning medical imaging |
| url | https://www.mdpi.com/2078-2489/16/7/615 |
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