Breast Cancer Detection using Deep Learning Model: Comparative Study

DOI

10.2991/978-94-6463-805-9_6How to use a DOI?

Keywords

Breast cancer; Mammography images; CNN; MobileNet

Abstract

The automatic detection of breast cancer is crucial for early diagnosis, monitoring disease progression, and verifying treatment effectiveness. Recently, deep learning has demonstrated increasing effectiveness due to the availability of diverse image databases and the emergence of new, more efficient models. In this article, we propose a breast cancer diagnostic approach through a comparative study of three deep learning models: MobileNet, and personalized CNN1 and CNN2. These models are tested on DDSM dataset and our collected dataset. The Custom CNN model demonstrated superior performance compared to other models, achieving an accuracy of 93.9%, and 88.3% on DDSM and our dataset.

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  - Sara Belarouci
AU  - Lamia Kazi Tani
AU  - Zineb Aziza Elaouaber
AU  - Souaad Hamza-Cherif
AU  - Mahammed Messadi
PY  - 2025
DA  - 2025/08/05
TI  - Breast Cancer Detection using Deep Learning Model: Comparative Study
BT  - Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
PB  - Atlantis Press
SP  - 41
EP  - 48
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-805-9_6
DO  - 10.2991/978-94-6463-805-9_6
ID  - Belarouci2025
ER  -