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Medical Ultrasound Image Segmentation Using U-Net Architecture

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Advances in Computing and Data Sciences (ICACDS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1613))

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Abstract

This research article discusses the implementation aspects of a Deep Learning architecture based on U-Net for medical image segmentation. A base model of the U-Net architecture is extended and experimented. Unlike the existing model, the input images are enhanced by applying a Non-Local Means filter optimized using a metaheuristic Grey wolf optimization method. Further, the model parameters are modified to achieve better performance. Tests were performed using two benchmark B-mode Ultrasound image datasets of 200 Breast lesion images and 504 Skeletal images. Experimental results demonstrate that the modifications resulted in more accurate segmentation. The performance of the modified implementation is compared with the base model and a Bidirectional Convolutional LSTM architecture.

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Authors and Affiliations

  1. Department of Computer Applications, MES College, Marampally, Kochi, India

    V. B. Shereena

  2. SMIEEE, Department of Computer Science and Engineering, Faculty of Engineering, Christ (Deemed to Be University), Bengaluru, India

    G. Raju

Authors
  1. V. B. Shereena
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  2. G. Raju
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Corresponding author

Correspondence to V. B. Shereena .

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Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, India

    P. K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

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Shereena, V.B., Raju, G. (2022). Medical Ultrasound Image Segmentation Using U-Net Architecture. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol 1613. Springer, Cham. https://doi.org/10.1007/978-3-031-12638-3_30

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  • DOI: https://doi.org/10.1007/978-3-031-12638-3_30

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  • Online ISBN: 978-3-031-12638-3

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