Comparative Analysis of Traditional, Hybrid, and Deep Learning Approaches for Breast Cancer Classification

DOI

10.2991/978-94-6463-740-3_26How to use a DOI?

Keywords

Breast Cancer; Machine Learning; Deep Neural Network; Predictive Analytics; Hybrid Voting Classifier

Abstract

This study compares classical Machine Learning (ML) models Logistic Regression, Random Forest, and Support Vector Machine with a Hybrid Voting Classifier and an enhanced Deep Neural Network (DNN) for breast cancer classification using the Breast Cancer Wisconsin dataset. In comparison to conventional models, the DNN greatly increases classification accuracy when tuned using dropout and batch normalization techniques. We evaluate each model’s performance using metrics such as accuracy, precision, recall, F1 score, and AUC. A radar chart visually presents the comparative results, highlighting the strengths and weaknesses of each approach. Our findings emphasize the effectiveness of deep learning in medical diagnostics while showcasing the competitive performance of traditional methods. This research contributes valuable insights into model selection for enhanced breast cancer detection and diagnosis.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Anjali Dwivedi
AU  - Nagendra Patel
AU  - Aditya Bhushan
PY  - 2025
DA  - 2025/06/25
TI  - Comparative Analysis of Traditional, Hybrid, and Deep Learning Approaches for Breast Cancer Classification
BT  - Proceedings of the 6th International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR 2024)
PB  - Atlantis Press
SP  - 303
EP  - 313
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-740-3_26
DO  - 10.2991/978-94-6463-740-3_26
ID  - Dwivedi2025
ER  -