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Quantum Optimized AlexNet for Histopathology Breast Image Diagnosis

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Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023 (AISI 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 184))

Abstract

Breast cancer is a prevalent and life-threatening disease affecting millions of women worldwide. Timely and accurate detection is crucial for improving patient outcomes. Deep learning techniques have shown remarkable success in image classification tasks, including breast cancer diagnosis. However, the integration of quantum computing into deep learning frameworks remains relatively unexplored. This paper investigates the potential of leveraging quantum computing to enhance image classification, particularly in breast cancer detection. The focus is on utilizing the “breakhis-400x” binary dataset to develop an advanced breast cancer image classifier. The proposed Quantum-Optimized AlexNet (QOA) approach, combines the feature extraction capabilities of the AlexNet model with a quantum layer acting as a linear layer. Experimental results on the BreakHis-400x dataset demonstrate the significant potential of the QOA model, achieving an overall accuracy of 93.67%. These findings highlight the utility of Quantum Computing in improving deep learning models for image classification, particularly in medical imaging analysis, and contribute to the advancement of precision medicine in breast cancer diagnosis.

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Authors and Affiliations

  1. School of Computer Science, Canadian International College (CIC), Cairo, Egypt

    Hamza Kamel Ahmed, Baraa Tantawi, Malak Magdy & Gehad Ismail Sayed

Authors
  1. Hamza Kamel Ahmed
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  2. Baraa Tantawi
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  3. Malak Magdy
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  4. Gehad Ismail Sayed
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Corresponding author

Correspondence to Gehad Ismail Sayed .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    AboulElla Hassanien

  2. Port Said University, Port Fouad City, Egypt

    Rawya Y. Rizk

  3. Department of Operations Research and Statistics, The University of Belgrade, Faculty of Organizational Sciences, Belgrade, Serbia

    Dragan Pamucar

  4. Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

  5. Shulin District, Fujian University of Technology, New Taipei, Taiwan

    Kuo-Chi Chang

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Ahmed, H.K., Tantawi, B., Magdy, M., Sayed, G.I. (2023). Quantum Optimized AlexNet for Histopathology Breast Image Diagnosis. In: Hassanien, A., Rizk, R.Y., Pamucar, D., Darwish, A., Chang, KC. (eds) Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023. AISI 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 184. Springer, Cham. https://doi.org/10.1007/978-3-031-43247-7_31

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