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Sparse deep belief network coupled with extended local fuzzy active contour model-based liver cancer segmentation from abdomen CT images

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Abstract

Liver cancer from abdominal CT images must be accurately segmented for the purpose of diagnosis with treatment planning. But, the similarity in gray values between the liver and the surrounding tissues poses a challenge. To address this, a novel sparse deep belief network coupled with extended local fuzzy active contour model-based liver cancer segmentation from abdomen CT images (SDBN-ELFAC-LCS-CT) is proposed. This method incorporates dynamic adaptive pooling and residual modules in SDBN to improve the feature selection and generalization ability. Additionally, the 3D reconstruction is performed to refine segmentation results. The proposed SDBN-ELFAC-LCS-CT approach is implemented in MATLAB. The performance of the proposed SDBN-ELFAC-LCS-CT achieves dice coefficients that were up to 96.16% higher and 75.88%, 88.75%, and 71.16% lower. Volumetric overlap error compared with existing models, like basic ensembles of vanilla-style deep learning modes, increases liver segmentation from CT imageries (BEVS-LCS-CT), an incorporated 3 dimensional sparse deep belief network along enriched seagull optimization approach for liver segmentation (3DBN-ESOA-LCS-CT) and iterative convolutional encoder-decoder network and multiple scale context learning for segmenting liver (ICEDN-LCS-CT), respectively.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Bethlahem Institute of Engineering, Karungal, Kanyakumari, 629157, Tamil Nadu, India

    A. Joel Dickson

  2. Department of Electrical and Electronics Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Kanyakumari, 629166, Tamil Nadu, India

    J. Arul Linsely

  3. Department of Electronics and Communication Engineering, Loyola Institute of Technology & Science, Kanyakumari, 629302, Tamil Nadu, India

    V. Antony Asir Daniel

  4. Department of Basic and Applied Science, NIFTEM, Kundli, Sonepat, 131028, India

    Kumar Rahul

Authors
  1. A. Joel Dickson
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  2. J. Arul Linsely
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  3. V. Antony Asir Daniel
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  4. Kumar Rahul
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Contributions

A. Joel Dickson (corresponding author)—conceptualization, methodology, and original draft preparation.

J. Arul Linsely—supervision

V. Antony Asir Daniel,—supervision

Kumar Rahul—supervision

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Correspondence to A. Joel Dickson.

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Dickson, A.J., Linsely, J.A., Daniel, V.A.A. et al. Sparse deep belief network coupled with extended local fuzzy active contour model-based liver cancer segmentation from abdomen CT images. Med Biol Eng Comput 62, 1361–1374 (2024). https://doi.org/10.1007/s11517-023-03001-y

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  • DOI: https://doi.org/10.1007/s11517-023-03001-y

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