Performance benchmarking of convolutional neural networks and ensemble machine learning techniques for automated mammographic breast cancer detection: A comparative study
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 Information Systems and Business Analysis, Aston Business School, Aston University, Birmingham, UK.
4 Department of Computer Science, Prairie View A&M University, Prairie View, Texas USA.
5 Department of Computing and Mathematical Sciences, University of Greenwich, Greenwich London, UK.'
World Journal of Advanced Engineering Technology and Sciences, 2024, 12(02), 808–831.
Article DOI: 10.30574/wjaets.2024.12.2.0349
Publication history:
Received on 07 July 2024; revised on 17 August 2024; accepted on 19 August 2024
Abstract:
Breast cancer remains a leading cause of mortality among women worldwide, making early and accurate detection crucial for improving patient outcomes. This study presents a comparative analysis of various machine learning algorithms—EfficientNet, DenseNet, ResNeXt-50, and Support Vector Machine (SVM)—in predicting breast cancer using mammography images. Utilizing a dataset of 5,339 mammograms from the Digital Database for Screening Mammography (DDSM), the models were trained and tested on two classes: benign and malignant lesions. The mammograms underwent preprocessing techniques, including image quality assessment, contrast enhancement, and artifact removal, to ensure high-quality data for model training. The performance of each model was evaluated using metrics such as accuracy, sensitivity, specificity, and ROC analysis. The results revealed that the EfficientNet model outperformed the other algorithms, achieving an accuracy of 95.23%, sensitivity of 96.67%, and specificity of 93.82%. In contrast, DenseNet exhibited the lowest performance, struggling with the correct classification of cancer cases. The comparative analysis highlights the strengths and weaknesses of each model, offering valuable insights into their potential clinical applications. This research underscores the importance of selecting the appropriate machine learning architecture to enhance the predictive accuracy of breast cancer detection and provides a foundation for integrating these models into clinical practice for personalized treatment planning. Future studies will focus on expanding the dataset and improving model generalizability to diverse patient populations.
Keywords:
Comparative; Analysis; Machine Learning; Algorithms; Breast; Cancer Prediction; Mammography Images
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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