Breast cancer image classification using DenseNet201 and AlexNet based deep transfer learning

DOI

10.2991/978-94-6463-496-9_11How to use a DOI?

Keywords

Ultrasound images; data augmentation; transfer learning; CNN; Alexnet; Densenet201

Abstract

Breast cancer poses a significant risk to women, as it can advance silently during its initial phases without evident symptoms. Early detection is crucial in mitigating this potential threat to one’s health. In the past several years, Convolutional Neural Networks (CNNs) have achieved notable progress in classifying breast cancer images. The accuracy and performance of automatically extracting complex features from images has improved, often surpassing previous advanced methods in this field. Furthermore, learning transfer facilitates the adaptation of complex models originally trained for one purpose to entirely new tasks. However, deep learning-based classification methods may have overfitting problems, particularly when the dataset is limited. This study uses a variety of convolutional models to examine how data augmentation methods, such as picture rotation or horizontal and vertical subject moving, affect transfer learning accuracy. For the DenseNet201 and AlexNet models, the experimental findings show a significant improvement in accuracy of around 3.5%.

Copyright

© 2024 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  - Nasser Edinne Benhassine
AU  - Abdelnour Boukaache
AU  - Djalil Boudjehem
PY  - 2024
DA  - 2024/08/31
TI  - Breast cancer image classification using DenseNet201 and AlexNet based deep transfer learning
BT  - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
PB  - Atlantis Press
SP  - 129
EP  - 143
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-496-9_11
DO  - 10.2991/978-94-6463-496-9_11
ID  - Benhassine2024
ER  -