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Cancer Identification in Enteric Nervous System Preclinical Images Using Handcrafted and Automatic Learned Features

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Abstract

Chronic degenerative diseases affect Enteric Neuron Cells (ENC) and Enteric Glial Cells (EGC) in shape and quantity. Thus, searching for automatic methods to evaluate when these cells are affected is quite opportune. In addition, preclinical imaging analysis is outstanding because it is non-invasive and avoids exposing patients to the risk of death or permanent disability. We aim to identify a specific cancer experimental model (Walker-256 tumor) in the Enteric Nervous System (ENS) cells. The ENS image database used in our experimental evaluation comprises 1248 images taken from thirteen rats distributed in two classes: control/healthy or sick. The images were created with three distinct contrast settings targeting different ENS cells: ENC, EGC, or both. We extracted handcrafted and non-handcrafted features to provide a comprehensive classification approach using SVM as the core classifier. We also applied Late Fusion techniques to evaluate the complementarity between feature sets obtained in different scenarios. In the best case, we achieved an F1-score of 0.9903 by combining classifiers built from different image types (ENC and EGC), using Local Phase Quantization (LPQ) features.

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Data Availability

The data presented in this study will be made available in due course on GitHub at https://github.com/gustavozf/ENS_dataset.

Notes

  1. Abbot Laboratory, Chicago, IL, USA.

  2. Olympus BX 41, Tokyo, Japan.

  3. Moticam® 2500 5.0 Mega Pixel–Motic China Group Co., Shanghai, China.

  4. The name here is used to reference the texture descriptor, being disassociated with the original author.

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Acknowledgements

We thank the support of the universities involved in the development of this research.

Funding

This research has been partly supported by the following Brazilian agencies: Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).

Author information

Authors and Affiliations

  1. Departamento de Informática, Universidade Estadual de Maringá, Av. Colombo, 5790, Maringá, Paraná, 87020-900, Brazil

    Gustavo Z. Felipe, Lucas O. Teixeira & Yandre M. G. Costa

  2. Instituto Federal do Paraná, Pinhais, Paraná, 83330-200, Brazil

    Rodolfo M. Pereira

  3. Departamento de Ciências Morfológicas, Universidade Estadual de Maringá, Maringá, Paraná, 87020-900, Brazil

    Jacqueline N. Zanoni

  4. Universidade Estadual do Oeste do Paraná, Francisco Beltrão, Paraná, 85601-970, Brazil

    Sara R. G. Souza

  5. Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Padova, 35122, Padova, Italy

    Loris Nanni

  6. Universidade Federal de Pernambuco, Recife, Pernambuco, 50740-560, Brazil

    George D. C. Cavalcanti

Authors
  1. Gustavo Z. Felipe
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  4. Jacqueline N. Zanoni
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  5. Sara R. G. Souza
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  8. Yandre M. G. Costa
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Contributions

Conceptualization, G.Z.F., J.N.Z. and Y.M.G.C.; methodology, G.Z.F., R.M.P. and Y.M.G.C.; validation, G.Z.F., L.O.T., L.F.M.P., L.N., S.R.G.S, J.N.Z., R.M.P. and Y.M.G.C.; investigation, G.Z.F., L.O.T. and L.N.; writing—original draft preparation, G.Z.F.; writing—review and editing, R.M.P., L.O.T., L.N., S.R.G.S, J.N.Z. G.D.C.C., and Y.M.G.C.; supervision, R.M.P. and Y.M.G.C.; project administration, Y.M.G.C.; All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Gustavo Z. Felipe.

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Conflict of interest

The authors declare no conflict of interest

Ethics approval

Ethical review and approval were waived for this study, due to the use of rats during the image acquisition phase of the dataset construction. The study follows the ethical principles under the terms set out in the Brazilian federal Law 11,794 (October 2008) and the Decree 66,689 (July 2009), established by the Brazilian Society of Science on Laboratory Animals (SBCAL). All the proceedings were submitted and approved by the Standing Committee on Ethics in Animals Experimentation of the State University of Maringá under the Protocol numbers 062/2012 (TW), 4462180216 (AIA) and 073/2014 (D).

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Not applicable

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All authors have read and agreed to the published version of the manuscript.

Code availability

The developed source codes will be made available in due course on GitHub at https://github.com/gustavozf/PRZ_3M. All of the third-party source codes, such as texture descriptors, may be found described in their original papers.

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Felipe, G.Z., Teixeira, L.O., Pereira, R.M. et al. Cancer Identification in Enteric Nervous System Preclinical Images Using Handcrafted and Automatic Learned Features. Neural Process Lett 55, 5811–5832 (2023). https://doi.org/10.1007/s11063-022-11114-y

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