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Brain Tumor Segmentation Based on Zernike Moments, Enhanced Ant Lion Optimization, and Convolutional Neural Network in MRI Images

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Metaheuristics and Optimization in Computer and Electrical Engineering

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1077))

Abstract

Gliomas that form in glial cells in the spinal cord and brain are the most aggressive and common kinds of brain tumors (intra-axial brain tumors) due to their rapid progression and infiltrative nature. The procedure of recognizing tumor margins from healthy tissues is still an arduous and time-consuming task in the clinical routine. In this study, a robust and efficient machine learning-based pipeline is suggested for brain tumor segmentation. Moreover, we employ four MRI modalities for increasing the final accuracy of the segmentation results, namely, Flair, T1, T2, and T1ce. Firstly, eight feature maps are extracted from each modality using the Zernike moments approach. The Zernike moments can create a feature map using two parameters, namely, n and m. So, by changing these values, we are able to generate different sets of edge feature maps. Then, eight edge feature maps for each modality are selected to produce a final feature map. Next, four original images are encoded into new four images to represent more unique and key information using the Local Directional Number Pattern (LDNP). As different encoded image leads to obtaining different final results and accuracies, the Enhanced Ant Lion Optimization (EALO) was employed to find the best possible set of feature maps for creating the best possible encoded image. Finally, a CNN model is utilized to explore significant details from the brain tissue more efficiently which accepts four input patches. Overall, the suggested framework outperforms the baseline methods regarding Dice score and Recall.

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Acknowledgement

This publication has emanated from research [conducted with the financial support of/supported in part by a grant from Science Foundation Ireland under Grant number No. 18/CRT/6183 and is supported by the ADAPT Centre for Digital Content Technology which is funded under the SFI Research Centres Programme (Grant 13/RC/2106/_P2), Lero SFI Centre for Software (Grant 13/RC/2094/_P2) and is co-funded under the European Regional Development Fund. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

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Authors and Affiliations

  1. Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran

    Abbas Bagherian Kasgari

  2. School of Computing, Faculty of Engineering and Computing, Dublin City University, Dublin, Ireland

    Ramin Ranjbarzadeh & Annalina Caputo

  3. Faculty of Medicine, Catholic University of Leuven (KU Leuven), Leuven, Belgium

    Soroush Baseri Saadi

  4. Lero and ADAPT Research Centres, School of Computer Science, University of Galway, Galway, Ireland

    Malika Bendechache

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  1. Abbas Bagherian Kasgari
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  2. Ramin Ranjbarzadeh
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  3. Annalina Caputo
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  4. Soroush Baseri Saadi
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  5. Malika Bendechache
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Correspondence to Ramin Ranjbarzadeh .

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Editors and Affiliations

  1. Department of Industrial Engineering, Ankara Yıldırım Beyazıt University (AYBU), Ankara, Türkiye

    Navid Razmjooy

  2. Department of Industrial Engineering, Ankara Yıldırım Beyazıt University (AYBU), Ankara, Türkiye

    Noradin Ghadimi

  3. Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India

    Venkatesan Rajinikanth

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Bagherian Kasgari, A., Ranjbarzadeh, R., Caputo, A., Baseri Saadi, S., Bendechache, M. (2023). Brain Tumor Segmentation Based on Zernike Moments, Enhanced Ant Lion Optimization, and Convolutional Neural Network in MRI Images. In: Razmjooy, N., Ghadimi, N., Rajinikanth, V. (eds) Metaheuristics and Optimization in Computer and Electrical Engineering. Lecture Notes in Electrical Engineering, vol 1077. Springer, Cham. https://doi.org/10.1007/978-3-031-42685-8_10

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