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RETRACTED ARTICLE: Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques

  • Intelligent Biomedical Data Analysis and Processing
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This article was retracted on 22 April 2024

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Abstract

Making an accurate diagnosis of nasopharyngeal carcinoma (NPC) disease is a challenging task that involves many parties such as radiology specialists often times need to delineate NPC boundaries on various tumor-bearing endoscopic images. It is a tedious and time-consuming operation exceedingly based on doctors and experience of radiologist. NPC has complex and irregular structures which makes it difficult to diagnose even by an expert physician. However, the diagnosis accuracy results of such methods are still insignificant and need improvement in order to manifest robust solution. The study aim is to develop and propose a new automatic classification of NPC tumor using machine learning techniques and feature-based decision-level fusion scheme from endoscopic images. We have implemented the fusion of the three image texture-based schemes (local binary patterns, the first-order statistics histogram properties, and histogram of gray scale) at the decision level and tested the performance of this scheme using the same experimental setup in the previous section for simple score-level fusion, but for comparison, We used the classifiers methods which are support vector machines (SVM), k-nearest neighbors’ algorithm, and artificial neural network (ANN). The results demonstrate that the majority rule for decision-based fusion is outperformed considerably by the single best performing feature scheme (FFGF) for the SVM classifier, but for the ANN and KNN classifier it is significantly outperformed by each of the components features. The classifiers approaches were listed a high accuracy of 94.07%, the sensitivity of 92.05%, and specificity of 93.07%.

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Acknowledgements

This research has been supported by fellowship scheme (UTeM Zamalah scheme) by Universiti Teknikal Malaysia Melaka, Malaysia.

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Authors and Affiliations

  1. Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Mohd Khanapi Abd Ghani, Mazin Abed Mohammed & M. A. Burhanuddin

  2. Planning and Follow Up Department, University Headquarter, University of Anbar, Anbar, Iraq

    Mazin Abed Mohammed & Dheyaa Ahmed Ibrahim

  3. School of EEE, Sastra University, Thanjavur, India

    N. Arunkumar

  4. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia

    Salama A. Mostafa

  5. Department of Otorinolaringologi, Hospital Pakar Sultanah Fatimah Muar, Muar, Johor, Malaysia

    Mohamad Khir Abdullah

  6. Dijlah University College, Baghdad, Iraq

    Mustafa Musa Jaber

  7. Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid, Jordan

    Enas Abdulhay

  8. Departamento de Telemática, Universidad del Cauca, Popayán, Cauca, 190002, Colombia

    Gustavo Ramirez-Gonzalez

Authors
  1. Mohd Khanapi Abd Ghani
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  2. Mazin Abed Mohammed
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  3. N. Arunkumar
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  4. Salama A. Mostafa
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  5. Dheyaa Ahmed Ibrahim
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  6. Mohamad Khir Abdullah
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  7. Mustafa Musa Jaber
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  8. Enas Abdulhay
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  9. Gustavo Ramirez-Gonzalez
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  10. M. A. Burhanuddin
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Corresponding author

Correspondence to Mazin Abed Mohammed.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s00521-024-09807-7

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Abd Ghani, M.K., Mohammed, M.A., Arunkumar, N. et al. RETRACTED ARTICLE: Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques. Neural Comput & Applic 32, 625–638 (2020). https://doi.org/10.1007/s00521-018-3882-6

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  • DOI: https://doi.org/10.1007/s00521-018-3882-6

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