Enhancing breast cancer detection accuracy through transfer learning: A case study using efficient net
1 Department of Computer and Information Sciences, Northumbria University London, United Kingdom
2 College of Business and Social Sciences, Aston University, Birmingham, UK.
3 Department of Electrical/ Electronic Engineering, University of Ibadan, Nigeria.
4 Department of Computing and Mathematical Sciences, University of Greenwich, Greenwich London, UK
5 Department of Physics, Joseph Sarwuan Tarka University, Makurdi, Nigeria.
6 Department of Information Systems and Business Analysis, Aston Business School, Aston, University, Birmingham, UK.
7 Department of Computer Management and Information Systems, Southern Illinois University, Edwardsville. USA.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 13(01), 285–318.
Article DOI: 10.30574/wjaets.2024.13.1.0415
Publication history:
Received on 31 July 2024; revised on 11 September 2024; accepted on 13 September 2024
Abstract:
Breast cancer continues to pose a significant global health challenge, emphasizing the need for advancements in early detection methods. This study explores the application of transfer learning techniques, specifically utilizing EfficientNet, to enhance the accuracy of breast cancer detection through medical imaging. Leveraging a dataset of mammography images from the Digital Database for Screening Mammography (DDSM), the research implements various data preprocessing methods, including median filtering, contrast enhancement, and artifact removal, to ensure the quality of input data. The EfficientNet model, trained with these preprocessed images, is evaluated against other transfer learning architectures, such as DenseNet and ResNeXt50, using metrics like accuracy, AUC, precision, and F1-score. The results demonstrate that EfficientNet outperforms other models, achieving an accuracy of 95.23%, with a sensitivity of 96.67% and specificity of 93.82%. These findings suggest that transfer learning, particularly with EfficientNet, can significantly improve the predictive accuracy of breast cancer detection, offering a reliable tool for early diagnosis and personalized treatment planning. The study also discusses the potential integration of these models into clinical workflows, addressing challenges such as data privacy, model generalizability, and clinical applicability. Future research will focus on expanding the dataset and exploring the use of other advanced deep learning techniques to further enhance detection accuracy and robustness.
Keywords:
Breast Cancer Detection; Transfer Learning; EfficientNet; Medical Imaging; Mammography; Machine Learning; Predictive Models; Deep Learning; Accuracy; Sensitivity; Specificity.
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0