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RETRACTED ARTICLE: Decision support system for nasopharyngeal carcinoma discrimination from endoscopic images using artificial neural network

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This article was retracted on 10 October 2022

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Abstract

The segregation among benign and malignant nasopharyngeal carcinoma (NPC) from endoscopic images is one of the most challenging issues in cancer diagnosis because of the many conceivable shapes, regions, and image intensities, hence, a proper scientific technique is required to extract the features of cancerous NPC tumors. In the present research, a neural network-based automated discrimination system was implemented for the identification of malignant NPC tumors. In the proposed technique, five different types of qualities, such as local binary pattern, gray-level co-occurrence matrix, histogram of oriented gradients, fractal dimension, and entropy, were first determined from the endoscopic images of NPC tumors and then the following steps were executed: (1) an enhanced adaptive approach was employed as the post-processing method for the classification of NPC tumors, (2) an assessment foundation was created for the automated identification of malignant NPC tumors, (3) the benign and cancerous cases were discriminated by using region growing method and artificial neural network (ANN) approach, and (4) the efficiency of the outcomes was evaluated by comparing the results of ANN. In addition, it was found that texture features had significant effects on isolating benign tumors from malignant cases. It can be concluded that in our proposed method texture features acted as a pointer as well as a help instrument to diagnose the malignant NPC tumors. In order to examine the accuracy of our proposed approach, 159 abnormal and 222 normal cases endoscopic images were acquired from 249 patients, and the classifier yielded 95.66% precision, 95.43% sensitivity, and 95.78% specificity.

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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

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

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

    Mazin Abed Mohammed

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

    N. Arunkumar

  4. Department of IT, College of Science and Technology, University of Human Development, Sulaymaniyah KRG, Iraq

    Raed Ibraheem Hamed

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

    Salama A. Mostafa

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

    Mohamad Khir Abdullah

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  1. Mazin Abed Mohammed
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  2. Mohd Khanapi Abd Ghani
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  5. Salama A. Mostafa
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  7. M. A. Burhanuddin
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Correspondence to Mazin Abed Mohammed.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s11227-022-04871-z"

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Mohammed, M.A., Abd Ghani, M.K., Arunkumar, N. et al. RETRACTED ARTICLE: Decision support system for nasopharyngeal carcinoma discrimination from endoscopic images using artificial neural network. J Supercomput 76, 1086–1104 (2020). https://doi.org/10.1007/s11227-018-2587-z

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  • DOI: https://doi.org/10.1007/s11227-018-2587-z

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