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Image Processing-based Resource-Efficient Transfer Learning Approach for Cancer Detection Employing Local Binary Pattern Features

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Abstract

Cancer is a life-threatening disease and has witnessed a substantial increase during the past few years. With a further expected increase in the future, it poses a huge challenge for the medical industry to design accurate detection approaches. Earlier and accurate detection of these cancers increases the surviving chances of patients. Computer-aided diagnosis (CAD) can provide significant help in assisting medical experts to make timely and accurate diagnoses. Deep learning models show great promise but are limited by the need for larger datasets for training and higher computational complexity. This study proposes a resource-efficient EfficienetNetB4 model that incorporates local binary pattern features to enhance the detection accuracy of cancer. The proposed approach integrates histopathological images and texture-based features with EfficientNetB4. Extensive experiments involving several pre-trained convolutional neural network variants are carried out. Results show the superior results of the proposed EfficientNetB4 model with a 99.8% accuracy. In addition, precision, recall, and F1 scores are 99.9% each thereby indicating the exceptional results for cancer detection exceeding the existing state-of-the-art approaches. Using a 5-fold cross-validation provides an average 99.88% accuracy further validating the performance of EfficientNetB4. Furthermore, the use of Shapley additive explanations helps comprehend the results and increases the transparency of the decision-making process. This study potentially contributes to further research in CAD-based resource-efficient cancer detection.

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Availability of data and materials

The data and code associated with this research can be obtained from the first, second, and corresponding authors at a suitable request.

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Acknowledgements

Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R348), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Funding

Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R348), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Author information

Author notes

  1. Muhammad Umer, Huihui Wang and Imran Ashraf contributed equally to this work.

Authors and Affiliations

  1. Department of Information Technology, College of Computer and Information Sciences, King Saud University, Riyadh 11421, P. O. Box 800, Saudi Arabia

    Ebtisam Abdullah Alabdulqader

  2. Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, 22230, Pakistan

    Muhammad Umer

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

    Khaled Alnowaiser

  4. Cybersecurity Program, St. Bonaventure University, New York, NY, 14778, USA

    Huihui Wang

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

    Aisha Ahmed Alarfaj

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

    Imran Ashraf

Authors
  1. Ebtisam Abdullah Alabdulqader
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  2. Muhammad Umer
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  3. Khaled Alnowaiser
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  4. Huihui Wang
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  5. Aisha Ahmed Alarfaj
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  6. Imran Ashraf
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Contributions

EAA conceived the idea, performed formal analysis and wrote the original manuscript. MU conceived the idea, performed data curation and wrote the original manuscript. KA designed the methodology and performed formal analysis and data curation. HW performed investigation, designed the methodology and performed project administration. AAA acquire the funding, dealt with software and performed visualization. IA supervised the work, performed validation and wrote-edited the manuscript. All authors reviewed the manuscript and approved it.

Corresponding authors

Correspondence to Aisha Ahmed Alarfaj or Imran Ashraf.

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Alabdulqader, E.A., Umer, M., Alnowaiser, K. et al. Image Processing-based Resource-Efficient Transfer Learning Approach for Cancer Detection Employing Local Binary Pattern Features. Mobile Netw Appl (2024). https://doi.org/10.1007/s11036-024-02331-x

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