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UCapsNet: A Two-Stage Deep Learning Model Using U-Net and Capsule Network for Breast Cancer Segmentation and
Golla Madhu1, Avinash Meher Bonasi1, Sandeep Kautish2
1Department of Information Technology, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad 500090, India.
Cancers
|November 27, 2024
Summary
A new UCapsNet model enhances breast cancer detection by combining U-Net segmentation with Capsule Network classification. This approach significantly improves diagnostic accuracy for early and reliable tumor identification in ultrasound images.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Breast cancer is a major global health concern for women.
- Ultrasound imaging aids tumor detection but often lacks detail for accurate diagnosis.
- Traditional U-Net models struggle with image quality, impacting early detection rates.
Purpose of the Study:
- To develop an advanced model for improved breast cancer detection in ultrasound images.
- To overcome the limitations of conventional U-Net models in identifying subtle tumor features.
- To enhance both segmentation and classification accuracy for more reliable diagnoses.
Main Methods:
- An enhanced U-Net model integrated with a Capsule Network (UCapsNet) was proposed.
- The model utilizes higher filter counts and skip connections for improved segmentation.
- A two-stage process involves U-Net segmentation followed by Capsule Network classification.
Main Results:
- UCapsNet achieved high performance metrics on the Breast Ultrasound Image (BUSI) dataset.
- Precision, recall, and accuracy rates were recorded at 98.12%, 99.52%, and 99.22%, respectively.
- The model outperformed established pre-trained models like VGG-19, DenseNet, and ResNet-50.
Conclusions:
- UCapsNet effectively combines segmentation and classification for enhanced diagnostic precision.
- The model addresses key weaknesses in existing breast cancer detection methods.
- Findings support UCapsNet's reliability for practical clinical applications in early tumor detection.
