What type of study is this?

This is a Quantitative Study study.

September 12, 2025International Journal of Image and Graphics

Intelligent BCDecNet Framework: Adaptive Segmentation with Optimized Multiscale Dilated Deep Residual Attention Network for Mammogram Breast Cancer Detection

View Full Paper

Ask AI

Bookmark

Authors

JSJ. SivamuruganPondicherry University SGSatyavratan GovindarajanNovartis (India)

Discussion

Member takes

Implication

This framework improves breast cancer detection using optimized segmentation and deep learning algorithms, revealing its potential impact on early diagnosis.

Key Points

The proposed framework achieves 96.50% accuracy in breast cancer detection, indicating significant advancements.
Precision and MCC metrics are also high, with values of 93.19% and 98.25%, respectively in the testing phase.
This study utilizes an Optimized Multiscale Dilated Deep Residual Attention Network to enhance breast cancer classification.
The Randomized Condition of Driving Training and Dingo Optimization is critical for tuning parameters within the segmentation technique.

Cite This Study

Sivamurugan et al. (2025) studied this question.

synapsesocial.com/papers/68d44b3031b076d99fa5483c https://doi.org/10.1142/s0219467827500690

View Full Paper

Ask AI

Bookmark

Implication

This framework improves breast cancer detection using optimized segmentation and deep learning algorithms, revealing its potential impact on early diagnosis.

Intelligent BCDecNet Framework: Adaptive Segmentation with Optimized Multiscale Dilated Deep Residual Attention Network for Mammogram Breast Cancer Detection

Discussion

Member takes

Implication

Key Points

Cite This Study

Intelligent BCDecNet Framework: Adaptive Segmentation with Optimized Multiscale Dilated Deep Residual Attention Network for Mammogram Breast Cancer Detection

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider