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Selective feature-based ovarian cancer prediction using MobileNet and explainable AI to manage women healthcare

  • 1235: Supporting Medicine and Healthcare with Multimedia Tools
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Abstract

Ovarian cancer is a global health concern due to the unavailability of an effective screening strategy and is often diagnosed at a late stage with approximately 70% of the case which reduces the survival chances of patients. Initial diagnosis is challenging due to inconspicuous symptoms in its initial stages which complicate its timely diagnosis. Regular screenings, such as pelvic exams, ultrasounds, and blood tests targeting specific biomarkers can be helpful for early diagnosis. In addition, the use of machine learning models can help automate this process thereby assisting medical experts for accurate diagnosis. This study aims at timely and accurate detection of ovarian cancer using a transfer learning approach that uses the MobileNet model. Moreover, the Chi-square technique is used to extract the most impactful features for better accuracy. For experiments, the Soochow University ovarian cancer dataset is employed. Extensive experiments are performed using all features from the dataset, as well as, using Chi-square-based selective features. The best accuracy of 90.88% is achieved with the Xception model when all features are used. Experimental results show a substantial increase when selective features are used indicating a 98.49% accuracy using the MobileNet model with 20 most important features. In addition, precision, recall, and F1 scores of 99.13%, 99.27%, and 99.20% are obtained showing the model’s robustness and generalization. This study also employs Shapley additive explanations to explain the importance of various features toward the model’s output thereby providing transparency for the model’s decision-making process.

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Data Availability

The datasets generated during and/or analyzed during the current study is available from the corresponding author on reasonable request.

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Acknowledgements

This research is funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R410), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. This study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2024/R/1445).

Funding

The authors are thankful to Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R410), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. This study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2024/R/1445).

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Authors and Affiliations

  1. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

    Nouf Abdullah Almujally

  2. Department of Computer Science and Engineering, College of Computer Science and Engineering, University of Hafr Al Batin, Hafr Al Batin, 39524, Saudi Arabia

    Abdulrahman Alzahrani

  3. Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Abeer M. Hakeem & Afraa Attiah

  4. Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur, Pakistan

    Muhammad Umer

  5. Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, P.O. Box 151, 11942, Saudi Arabia

    Shtwai Alsubai

  6. Department of Management & Innovation Systems, University of Salerno, Fisciano, 84084, Italy

    Matteo Polsinelli

  7. Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea

    Imran Ashraf

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  1. Nouf Abdullah Almujally
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  2. Abdulrahman Alzahrani
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  3. Abeer M. Hakeem
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  4. Afraa Attiah
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  5. Muhammad Umer
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Correspondence to Muhammad Umer or Imran Ashraf.

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Almujally, N.A., Alzahrani, A., Hakeem, A.M. et al. Selective feature-based ovarian cancer prediction using MobileNet and explainable AI to manage women healthcare. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19286-6

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  • DOI: https://doi.org/10.1007/s11042-024-19286-6

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