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Tumor edge detection in mammography images using quantum and machine learning approaches

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Abstract

Automatic processing and analysis of medical images may provide to doctor valuable assistance for diagnostic and therapeutic practice. In this work, the problem of breast cancer edge detection is addressed. We are faced with a challenging task considering the breast tissue specificities and the inevitable mammogram noise. To meet this challenge, we propose novel approaches involving quantum genetic algorithm and support vector machines. The first method uses the quantum genetic algorithm to solve a multilevel thresholding problem based on Tsallis entropy. In the second method, the support vector machines are trained, in different ways, on a simulated image in order to be able to detect breast cancer edge. The proposed approaches are compared to some standard methods of edge detection on a sample of mammographic images taken from a well-known benchmark databases. The evaluation results obtained by PSNR, SSIM and FSIM metrics demonstrated the effectiveness of the proposed approaches.

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Acknowledgements

This Project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under Grant No. G:1554-612-1440. The authors, therefore, acknowledge with thanks DSR for technical and financial support.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

    Amani Tariq Jamal

  2. Department of Quantitative Methods, Higher Institute of Management, University of Tunis, Tunis, Tunisia

    Anis Ben Ishak

  3. Department of Mathematics, Faculty of Science, Sohag University, Sohag, Egypt

    Sayed Abdel-Khalek

  4. Department of Mathematics, Faculty of Science, Taif University, Taif, Saudi Arabia

    Sayed Abdel-Khalek

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  1. Amani Tariq Jamal
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  2. Anis Ben Ishak
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  3. Sayed Abdel-Khalek
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Correspondence to Sayed Abdel-Khalek.

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Tariq Jamal, A., Ben Ishak, A. & Abdel-Khalek, S. Tumor edge detection in mammography images using quantum and machine learning approaches. Neural Comput & Applic 33, 7773–7784 (2021). https://doi.org/10.1007/s00521-020-05518-x

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  • DOI: https://doi.org/10.1007/s00521-020-05518-x

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