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An efficient computer-aided diagnosis model for classifying melanoma cancer using fuzzy-ID3-pvalue decision tree algorithm

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Abstract

Visual observation and dermoscopic analysis are the most common methods of diagnosing skin cancer. In advanced stages, melanomas spread faster and are less responsive to treatment. Because different lesions in the skin appear similar to one another and sometimes errors in identification occur, the accuracy of diagnosis will decrease significantly when the amount of received images is large. However, the proposed methods for estimating skin lesions and their separation from melanoma have uncertainties and are not generalizable. This paper proposes an optimal decision tree (DT)-based approach, including fuzzy-ID3-pValue and Bayes learning algorithms, which overcomes these challenges. When classifying images, DTs employ a multi-stage procedure to partition the feature space, which enhances their ease of use, precision, and speed. Inference engines are used in fuzzy logic to derive logical deductions about knowledge, which facilitates learning DT and Bayesian learning. Taking advantage of the DT dependency structure, we present a novel fuzzy DT for extracting precise and collaborative fuzzy rules. Furthermore, to emphasize the cohesive nature of the laws, a weighted method is employed. Besides, the inference engine system is constructed through deductive and inductive inference engines. The proposed method is verified using the ISIC-2019 dataset as well as the PH2 images, both of which contain dermoscopic images of multiple lesions. The proposed method provides efficient results with 96% and 88% accuracy for ISIC-2019 and PH2 data, respectively.

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Data availability

All the data and codes are available through corresponding authors.

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Acknowledgement

This work was supported by Islamic Azad University with the grant number 133713281361.

Funding

The information regarding funding is inapplicable / No funds was obtained.

Author information

Authors and Affiliations

  1. Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy

    Hamidreza Rokhsati

  2. Department of Biomedical Engineering, Meybod University, Meybod, Iran

    Khosro Rezaee

  3. Department of Earth and Marine Sciences, University of Palermo, Palermo, Italy

    Aaqif Afzaal Abbasi

  4. Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Ar-Rayyan, Qatar

    Samir Brahim Belhaouari

  5. Department of Computer engineering and information, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, 11991, Wadi Ad-Dwasir, Saudi Arabia

    Jana Shafi

  6. Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China

    Yang Liu

  7. Institute of Artificial Intelligence, Shaoxing University, Zhajiang, China

    Mehdi Gheisari

  8. Department of Cognitive Computing, Institute of Computer Science and Engineering, Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences, Chennai, India

    Mehdi Gheisari

  9. Department of Computer Science, Islamic Azad University, Shiraz, Iran

    Mehdi Gheisari

  10. Department of R&D, Shenzhen BKD Co.LTD, Shenzhen, China

    Mehdi Gheisari

  11. Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

    Ali Akbar Movassagh

  12. Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China

    Saeed Kosari

Authors
  1. Hamidreza Rokhsati
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Correspondence to Khosro Rezaee or Mehdi Gheisari.

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Rokhsati, H., Rezaee, K., Abbasi, A.A. et al. An efficient computer-aided diagnosis model for classifying melanoma cancer using fuzzy-ID3-pvalue decision tree algorithm. Multimed Tools Appl 83, 76731–76751 (2024). https://doi.org/10.1007/s11042-024-18314-9

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