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In this study, we proposed an automatic segmentation method based on convolutional neural networks (CNNs), by developing a new model using the Resnet50 architecture for detection and the DrvU-Net architecture, derived from the U-Net model, with adjustments adapted to the characteristics of the medical imaging data for the segmentation of a publicly available brain image dataset called TCGA-LGG and TCIA. Following an in-depth comparison with other recent studies, our model has demonstrated its effectiveness in the detection and segmentation of brain tumours, with accuracy rates for accuracy and the Dice Similarity Coefficient (DSC), the Similarity Index (IoU) and the Tversky Coefficient reaching 96%, 94%, 89% and 91.5% respectively.<\/jats:p>","DOI":"10.3233\/idt-240385","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T13:35:40Z","timestamp":1722605740000},"page":"2079-2096","source":"Crossref","is-referenced-by-count":3,"title":["Advancing brain tumour segmentation: A novel CNN approach with Resnet50 and DrvU-Net: A comparative study"],"prefix":"10.1177","volume":"18","author":[{"given":"Kamal","family":"Halloum","sequence":"first","affiliation":[]},{"given":"Hamid","family":"Ez-Zahraouy","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"key":"10.3233\/IDT-240385_ref1","doi-asserted-by":"publisher","first-page":"3983","DOI":"10.32604\/cmc.2022.030923","article-title":"A novel inherited modeling structure of automatic brain tumor segmentation from MRI","volume":"73","author":"Asiri","year":"2022","journal-title":"Comput Mater Contin"},{"key":"10.3233\/IDT-240385_ref2","doi-asserted-by":"publisher","first-page":"5735","DOI":"10.32604\/cmc.2022.031747","article-title":"Block-wise neural network for brain tumor identification in magnetic resonance images","volume":"73","author":"Asiri","year":"2022","journal-title":"Comput Mater Contin"},{"key":"10.3233\/IDT-240385_ref3","doi-asserted-by":"crossref","unstructured":"de Biase D, Franceschi E, Marucci G. 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