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For the Nuclei Segmentation of Liver Cancer Histopathology Images, A Deep Learning Detection Approach is Used

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Engineering Applications of Artificial Intelligence

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Abstract

One of the cancers that causes the greatest mortality is liver cancer worldwide. Consequently, early identification and detection of potential Cancer mortality is decreased thanks to liver cancer. Traditionally, Histopathological Image Analysis (HIA) was performed, however these take a lot of time and require in-depth understanding. We the segmentation and classification of liver cells is advised to use a patch-based deep learning approach. In this work, complete slides are categorized and divided using a two-step process (WSI is a suggested image). WSIs must first be extracted into patches since they stand besides huge toward stay input directly interested in convolutional neural networks (CNN). Supplying the patches to a modified U-Net through its comparable veneer for targeted segmentation. For arrangement responsibilities the WSIs are mounted at 4, equivalent to 3x, 16x, and 64x. Each scale’s deleted patches and associated labels are then fed into the convolutional network. Inference is a process where we majority voting on the convolutional neural network’s output network. Better outcomes have been seen with the suggested strategy. Whole-slide image, segmentation, classification, and patch-based methods for histopathological image analysis.

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Author information

Authors and Affiliations

  1. Department of Computer Science Faculty of Computing and Artificial, Intelligent Air University, Islamabad, Pakistan

    Arifullah

  2. Faculty of Law, Economics and Social Sciences (Ain Chock), Hassan II University, Casablanca, Morocco

    Aziza Chakir

  3. Cristal Laboratory, National School of Computer Sciences, University of Manouba, Manouba, Tunisia

    Dorsaf Sebai

  4. Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, Pakistan

    Abdu Salam

Authors
  1. Arifullah
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  2. Aziza Chakir
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  3. Dorsaf Sebai
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  4. Abdu Salam
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Corresponding author

Correspondence to Arifullah .

Editor information

Editors and Affiliations

  1. Faculty of Law, Economics and Social Sciences, Hassan II University, Casablanca, Morocco

    Aziza Chakir

  2. Information Systems, Universitas Bunda Mulia, Jakarta, Indonesia

    Johanes Fernandes Andry

  3. Department of Computer Science Faculty of Computing and Artificial Intelligence, Air University, Islamabad, Pakistan

    Arif Ullah

  4. Department of Management Studies, Vaish College of Engineering, Rohtak, India

    Rohit Bansal

  5. Department of Mathematics and Computer Science, University of Hassan II, Casablanca, Morocco

    Mohamed Ghazouani

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Arifullah, Chakir, A., Sebai, D., Salam, A. (2024). For the Nuclei Segmentation of Liver Cancer Histopathology Images, A Deep Learning Detection Approach is Used. In: Chakir, A., Andry, J.F., Ullah, A., Bansal, R., Ghazouani, M. (eds) Engineering Applications of Artificial Intelligence. Synthesis Lectures on Engineering, Science, and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-50300-9_14

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  • DOI: https://doi.org/10.1007/978-3-031-50300-9_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50299-6

  • Online ISBN: 978-3-031-50300-9

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