What type of study is this?

This is a Quantitative Study study.

Enhancing Breast Cancer Segmentation in Mammography with UNet++ - Deep Learning Approach

View Full Paper

Ask AI

Bookmark

Authors

Automatic segmentation improves accuracy in mammograms for breast cancer, indicating enhanced detection potential.

The proposed method achieves a precision rate of 92.33% for segmenting breast masses in mammograms.
Using evaluation metrics, the method shows a True Positive Rate of 93.83% across datasets, indicating high reliability.
UNet++ is utilized within a comprehensive pre-processing pipeline, which enhances image quality for improved segmentation.
Results on the INbreast dataset confirm the model's generalizability, with a Jaccard Index of 87.25% on unseen data.

Jalalian et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark

View Full Paper

Ask AI

Bookmark

Authors

Automatic segmentation improves accuracy in mammograms for breast cancer, indicating enhanced detection potential.

The proposed method achieves a precision rate of 92.33% for segmenting breast masses in mammograms.
Using evaluation metrics, the method shows a True Positive Rate of 93.83% across datasets, indicating high reliability.
UNet++ is utilized within a comprehensive pre-processing pipeline, which enhances image quality for improved segmentation.
Results on the INbreast dataset confirm the model's generalizability, with a Jaccard Index of 87.25% on unseen data.

Jalalian et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark