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Convolution neural network based model to classify colon cancerous tissue

  • 1218: Engineering Tools and Applications in Medical Imaging
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Abstract

Computer vision-based methods play a significant role in the recognition of cancerous tissue from histopathological images. Therefore, computer-assisted diagnosis systems provide an effective system for medical diagnosis. At the same time, conventional medical image processing methods rely on feature extraction algorithms suited for a particular problem. However, deep learning-based methods are becoming vital alternatives with new developments in the machine learning area to reduce the complications of the feature-based methods. Therefore, a Convolutional Neural Network-based model has been proposed to categorize colon cancer histopathological images having multiple classes of cancerous tissues. Eight different classes of cancer have been examined, namely tumor epithelium, simple stroma, immune cells, complex stroma, normal mucosal glands, debris, adipose tissue, and background. The Convolution Neural Network (ConvNet) classification model is evaluated with four distinct activation layers, namely Rectified Linear Unit (ReLU), Leaky Rectified Linear Unit (LReLU), Parametrized Rectified Linear Unit (PReLU), and Exponential Linear Unit (ELU). Based on the produced results, the ELU activation function has shown the highest classification accuracy with on average 98% and in some cases 99%.

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Authors and Affiliations

  1. Computer Science and Information Systems, College of Computer Science and Engineering, University of Ha’il, Ha’il, Kingdom of Saudi Arabia

    Kusum Yadav & Jalawi Alshudukhi

  2. School of Computer Science, University of Petroleum and Energy Studies, Bidholi, Dehradun, 248007, Uttarakhand, India

    Shamik Tiwari & Anurag Jain

Authors
  1. Kusum Yadav
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  2. Shamik Tiwari
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  3. Anurag Jain
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  4. Jalawi Alshudukhi
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Correspondence to Shamik Tiwari.

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Yadav, K., Tiwari, S., Jain, A. et al. Convolution neural network based model to classify colon cancerous tissue. Multimed Tools Appl 81, 37461–37476 (2022). https://doi.org/10.1007/s11042-022-13504-9

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  • DOI: https://doi.org/10.1007/s11042-022-13504-9

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