Hybrid Ann-Curvelet-Densenet Framework for Brain Tumor Mri Classification and Segmentation

View Full Paper

Ask AI

Bookmark

Authors

Discussion

A hybrid approach improves classification and segmentation of brain tumors in MRI scans, suggesting enhanced diagnostic accuracy.

Achieving 96.3% accuracy, the model effectively classified and segmented brain tumors from MRI images.
Using metrics like precision and recall, the new model surpassed VGG16, ResNet50, and U-Net in performance.
The approach combines ANN, FDCT, and DenseNet for robust feature extraction and classification.
High computational complexity was addressed through dimensionality reduction and efficient feature processing.

Kanakambaran et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark

View Full Paper

Ask AI

Bookmark

Authors

A hybrid approach improves classification and segmentation of brain tumors in MRI scans, suggesting enhanced diagnostic accuracy.

Achieving 96.3% accuracy, the model effectively classified and segmented brain tumors from MRI images.
Using metrics like precision and recall, the new model surpassed VGG16, ResNet50, and U-Net in performance.
The approach combines ANN, FDCT, and DenseNet for robust feature extraction and classification.
High computational complexity was addressed through dimensionality reduction and efficient feature processing.

Kanakambaran et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark