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An Improved Model for Semantic Segmentation of Brain Lesions Using CNN 3D

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Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 716))

Abstract

The implementation of accurate automatic algorithms for brain tumor segmentation could improve disease diagnosis, treatment monitoring, and make large-scale studies of the pathology possible. In this study, we optimize the architecture used for the challenging task of brain lesion segmentation, which is based on the 3D convolutional neural network. In order to integrate broader local and contextual information, we use a two-channel architecture that processes input images at multiple scales simultaneously. Furthermore, we show that optimizing at the momentum value level, helps us achieve better results in the DSC, accuracy and sensitivity criteria. Our method has also been tested by the BRATS 2015 training dataset, where it has performed very well despite the simplicity of the method.

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Author information

Authors and Affiliations

  1. National Engineering School of Sfax, University of Sfax, 2100, Gafsa, Tunisia

    Ala Guennich

  2. Research Lab: Technology, Energy, and Innovative Materials Lab, Faculty of Sciences of Gafsa, University of Gafsa, Gafsa, Tunisia

    Ala Guennich & Mohamed Othmani

  3. Faculty of Sciences of Gafsa, University of Gafsa, 2100, Gafsa, Tunisia

    Mohamed Othmani

  4. Faculty of Sciences and Technology of Sidi Bouzid, 9100, Sidi Bouzid, Tunisia

    Hela Ltifi

  5. Research Lab: RGIM-Lab ENIS, Gafsa, Tunisia

    Hela Ltifi

Authors
  1. Ala Guennich
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  2. Mohamed Othmani
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  3. Hela Ltifi
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Corresponding author

Correspondence to Ala Guennich .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

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Guennich, A., Othmani, M., Ltifi, H. (2023). An Improved Model for Semantic Segmentation of Brain Lesions Using CNN 3D. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-031-35501-1_18

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