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

Being the most common type of cancer worldwide, and affecting over 2.3 million women, breast cancer poses a significant health threat. Although survival rates have improved around the world due to advances in screening, diagnosis, and treatment, early detection remains crucial for effective management. This study seeks to introduce a novel hybrid model that makes use of image-preprocessing techniques and deep-learning algorithms on mammograms to enhance the detection and classification accuracy of breast cancer lesions. The model was tested on a dataset comprising 20,000 mammograms. First, image-processing techniques, such as Contrast-Limited Adaptive Histogram Equalization, Gaussian Blur, and sharpening methods were used to optimize the images for enhanced feature extraction. In addition, the Ensemble Deep Random Vector-Functional Link Neural Network algorithm, YOLOv5, and MedSAM segmentation models were utilized for robust deep learning-based extraction, classification, and visualization of lesions. Finally, the model was clinically validated on 800 patients. The study found a notable enhancement in both accuracy and processing time for benign and malignant diagnoses using the hybrid model. The model achieves an impressive accuracy of 99.7 % and demonstrates a remarkable processing time of 0.75 s. In clinical applications, the hybrid model exhibits high proficiency, reporting 97.2 % accuracy for benign cases and 98.6 % for malignant scenarios. These results highlight the effectiveness of the hybrid model in improving diagnostic accuracy, offering a promising tool for early breast cancer detection.