On the use of Transfer Learning to Improve Breast Cancer Detection

Abstract

Breast cancer is one of the most common malignancies in women globally, and early identification is critical for better patient outcomes. Deep learning has developed in recent years as a promising approach for automating the identification of breast cancer in mammograms. Transfer learning, which involves adapting a pre-trained model to a new task, is a promising method for enhancing the efficiency and accuracy of breast cancer diagnosis using deep learning. This work studies the efficacy of transfer learning strategies in detecting breast cancer using pre-trained deep-learning models. Using large mammographic datasets, we investigate several transfer learning algorithms and assess their effects on detection performance metrics such as accuracy, precision, recall and ROC AUC. The study's results enhance automated breast cancer detection and shed light on how well transfer learning strategies can improve the precision and dependability of detection.

Keywords: Breast cancer, transfer learning, medical imaging, deep learning.

Received Date: April 05, 2024                  Accepted Date: May 14, 2024            

Published Date: June 01, 2024

Available Online at: https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/149

DOI:https://doi.org/10.5281/zenodo.11217339