This study presents a hybrid artificial intelligence model based on deep learning approaches to improve breast cancer detection and classification using mammographic images. Image preprocessing techniques, including CLAHE, Gaussian blur, and sharpening methods, were applied to enhance image quality, while advanced deep learning algorithms such as Ensemble Deep Random Vector Functional Link Neural Network, YOLOv5, and MedSAM were integrated for feature extraction, classification, and lesion visualization. The proposed model demonstrated high diagnostic performance for benign and malignant cases, highlighting its potential as a computer-aided diagnostic tool for early breast cancer detection and clinical decision support.

Title

Hybrid ensemble deep learning model for advancing breast cancer detection and classification in clinical applications

Publisher

Elsevier

Publisher Country

Netherlands

Indexing

Scopus

Impact Factor

3.6

Publication Type

Online only

Volume

10

Year

2024

Pages

e38374