Breast cancer classification using breast ultrasound images with a hybrid of transfer learning and Bayesian-optimized fast learning network
Abstract Breast cancer is the most prevalent cancer among women globally, with over 2.3 million new cases reported annually, hence, early and accurate diagnosis is crucial in minimizing mortality rates. In light of the limitation on the conventional interpretation of images, this study presents a no...
Saved in:
| Main Authors: | Emmanuel Ahishakiye, Fredrick Kanobe |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-05-01
|
| Series: | Discover Artificial Intelligence |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44163-025-00335-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Optimizing cervical cancer classification using transfer learning with deep gaussian processes and support vector machines
by: Emmanuel Ahishakiye, et al.
Published: (2024-10-01) -
Comparison of Transfer Learning Model Performance for Breast Cancer Type Classification in Mammogram Images
by: Cahya Bagus Sanjaya, et al.
Published: (2025-02-01) -
Evaluating Deep Learning Architectures for Breast Tumor Classification and Ultrasound Image Detection Using Transfer Learning
by: Christopher Kormpos, et al.
Published: (2025-04-01) -
Optimizing Breast Cancer Classification: A Comparative Analysis of Supervised and Unsupervised Machine Learning Techniques
by: Prithwish Ghosh, et al.
Published: (2024-04-01) -
Application of VGG16 Transfer Learning for Breast Cancer Detection
by: Tanjim Fatima, et al.
Published: (2025-03-01)