Breast Cancer Detection and Prediction Using Machine Learning and Image Processing

DOI

10.2991/978-94-6463-858-5_36How to use a DOI?

Keywords

Breast Cancer; Machine Learning; Deep Learning; Random Forest; CNN

Abstract

Breast cancer is a deadly illness affecting women worldwide which makes early and precise identification is crucial. To increase diagnostic accuracy, this work presents a dual strategy that integrates deep learning (DL) and machine learning (ML) algorithms. Random Forest model is used to examine tabular data and differentiate between cancerous and non-cancerous tumours based on many features like size, shape, and texture of tumour. Deep learning classifies breast mammography images in a binary manner using a Convolutional Neural Network (CNN). Pre-processing techniques like data augmentation and pixel normalization are used to make the model more robust. Well pre-processed data, feature extraction and testing multiple algorithms to find the best one for the problem statement ensure the reliability of both models.

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  - Sandip Shinde
AU  - Radha Waman
AU  - Dhanashri Wankhede
AU  - Shreya Wanwe
AU  - Isha Sable
PY  - 2025
DA  - 2025/11/04
TI  - Breast Cancer Detection and Prediction Using Machine Learning and Image Processing
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 405
EP  - 418
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_36
DO  - 10.2991/978-94-6463-858-5_36
ID  - Shinde2025
ER  -