Efficient Breast Cancer Classification from Histopathology Using Tuned DenseNet-121 and Image Augmentation

Authors

  • Sk Karishma Banu ,J Sudhakar babu Author

DOI:

https://doi.org/10.48047/mhjdsd25

Keywords:

Breast cancer, deep learning, histopathological image analysis, DenseNet-121, CNN, transfer learning, computer aided diagnosis, Streamlit

Abstract

Breast cancer is a leading cause of cancer-related mortality among women, and early detection remains critical for improving patient outcomes. This study introduces a deep learning-based method for the automated classification of breast cancer from histopathological images. 

Downloads

Download data is not yet available.

Downloads

Published

25.04.2025

Issue

Vol. 15 No. 4 (2025): volume 15 issue 4(2025)

Section

Articles

License

You are free to:

  1. Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
  2. Adapt — remix, transform, and build upon the material for any purpose, even commercially.
  3. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  1. Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  2. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

How to Cite

Efficient Breast Cancer Classification from Histopathology Using Tuned DenseNet-121 and Image Augmentation. (2025). International Journal of Information and Electronics Engineering, 15(4), 629-635. https://doi.org/10.48047/mhjdsd25