Towards Early Breast Cancer Detection: A Deep Learning Approach

Authors

  • Amina Bekkouche LRIT Laboratory, Faculty of Science, Department of Computer Science, University of Tlemcen, Algeria
  • Mohammed Merzoug LRIT Laboratory, Faculty of Science, Department of Computer Science, University of Tlemcen, Algeria
  • Mourad Hadjila STIC Laboratory, Faculty of Technology, Department of Telecommunication, University of Tlemcen, Algeria
  • Wafaa Ferhi STIC Laboratory, Faculty of Technology, Department of Telecommunication, University of Tlemcen, Algeria
Volume: 14 | Issue: 5 | Pages: 17517-17523 | October 2024 | https://doi.org/10.48084/etasr.8634

Received: 5 August 2024 | Revised: 31 August 2024 | Accepted: 4 September 2024 | Online: 9 October 2024


Corresponding author: Amina Bekkouche

Abstract

Early detection of breast cancer is crucial for patients' recovery chances to be improved. Artificial intelligence techniques, and more particularly Deep Learning (DL), may contribute to enhancing the accuracy of this detection. The main objective of this paper is to propose a DL model in an attempt to detect and classify breast cancer, and thus help people suffering from this disease. The Breast Cancer Wisconsin dataset was implemented to train neural networks, and their performance was subsequently evaluated on certain test datasets. The findings revealed that this approach provides promising results in terms of detection accuracy, with high sensitivity and specificity. The study also compares the performance of this approach with other breast cancer detection works, demonstrating that DL can provide significantly better results.

Keywords:

Breast cancer, Deep learning, Classification, Wisconsin dataset, Hyperparameters, Evaluation

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How to Cite

[1]
Bekkouche, A., Merzoug, M., Hadjila, M. and Ferhi, W. 2024. Towards Early Breast Cancer Detection: A Deep Learning Approach. Engineering, Technology & Applied Science Research. 14, 5 (Oct. 2024), 17517–17523. DOI:https://doi.org/10.48084/etasr.8634.

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Copyright (c) 2024 Amina Bekkouche, Mohammed Merzoug, Mourad Hadjila, Wafaa Ferhi

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