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Inception-UDet: An Improved U-Net Architecture for Brain Tumor Segmentation

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Abstract

Brain tumor segmentation is an important field and a sensitive task in tumor diagnosis. The treatment research in this area has helped specialists in detecting the tumor’s location in order to deal with it in its early stages. Numerous methods based on deep learning, have been proposed, including the symmetric U-Net architectures, which revealed great results in the medical imaging field, precisely brain tumor segmentation. In this paper, we proposed an improved U-Net architecture called Inception U-Det inspired by U-Det. This work aims at employing the inception block instead of the convolution one used in the bi-directional feature pyramid neural (Bi-FPN) network during the skip connection U-Det phase. Furthermore, a comparison study has been performed between our proposed approach and the three known architectures in medical imaging segmentation; U-Net, DC-Unet, and U-Det. Several segmentation metrics have been computed and then taken into account in these methods, by means of the publicly available BraTS datasets. Thus, our obtained results have showed promising results in terms of accuracy, dice similarity coefficient (DSC), and intersection–union ratio (IOU). Moreover, the proposed method has achieved a DSC of 87.9%, 85.5%, and 83.9% on BraTS2020, BraTS2018, and BraTS2017, respectively, calculated from the best fold in fourfold cross-validation employed in the present approach.

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Data is available on the Kaggle platform.

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Funding

The authors did not receive support from any organization for the submitted work.

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Author notes

  1. Jamal Riffi, Adnane Mohamed and Hamid Tairi have contributed equally to this work.

Authors and Affiliations

  1. LISAC Laboratory, Department of Computer Science, Faculty of Sciences Dhar El Mahraz, University Sidi Mohamed Ben Abdellah, 30000, Fez, Morocco

    Ilyasse Aboussaleh, Jamal Riffi, Adnane Mohamed Mahraz & Hamid Tairi

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  1. Ilyasse Aboussaleh
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  2. Jamal Riffi
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  3. Adnane Mohamed Mahraz
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  4. Hamid Tairi
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IA and JR are the contributors, IA is the coder and the writer, JR, MAM, and HT are the reviewers.

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Correspondence to Ilyasse Aboussaleh.

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Aboussaleh, I., Riffi, J., Mahraz, A.M. et al. Inception-UDet: An Improved U-Net Architecture for Brain Tumor Segmentation. Ann. Data. Sci. 11, 831–853 (2024). https://doi.org/10.1007/s40745-023-00480-6

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  • DOI: https://doi.org/10.1007/s40745-023-00480-6

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