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Performance Improvement of Breast Cancer Diagnosis Using Artificial Intelligence and Image Processing Techniques

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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

One of the most deadly and terrible diseases in the world that kills women is breast cancer. The timely detection of breast cancer can significantly impact and potentially save the lives of numerous women. Radiologists and clinicians have turned to computer-aided diagnosis as the burden of diagnosing breast cancer has grown. Based on thermal imaging, machine learning, and deep learning are employed in this study to diagnose breast cancer. Three stages—preprocessing, features extraction, and artificial neural network classifier—are used to investigate the proposed technique. Additionally, four Deep Learning models—AlexNet, GoogleNet, SqueezNet, and ResNet18—are suggested to attain high performance and accurate breast cancer diagnosis. The Machine Learning algorithm achieves accuracy of 89,74%, sensitivity of 82,35%, and specificity of 95,45% in simulation results. Additionally, Deep Learning models, like AlexNet, achieve accuracy levels of 100%.

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Authors and Affiliations

  1. Electrical Engineering Department, Faculty of Engineering, Suez Canal University, Ismailia, Egypt

    Rasha Sameh, Basem E. Elnaghi, Atef Ghuneim & Ahmed Magdy

Authors
  1. Rasha Sameh
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  2. Basem E. Elnaghi
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  3. Atef Ghuneim
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  4. Ahmed Magdy
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Corresponding author

Correspondence to Rasha Sameh .

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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Sameh, R., Elnaghi, B.E., Ghuneim, A., Magdy, A. (2023). Performance Improvement of Breast Cancer Diagnosis Using Artificial Intelligence and Image Processing Techniques. 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_48

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