Enhancing early breast cancer diagnosis through automated microcalcification detection using an optimized ensemble deep learning framework

Background: Breast cancer remains a pressing global health concern, necessitating accurate diagnostics for effective interventions. Deep learning models (AlexNet, ResNet-50, VGG16, GoogLeNet) show remarkable microcalcification identification (>90%). However, distinct architectures and methodologies pose challenges. We propose an ensemble model, merging unique perspectives, enhancing precision, and understanding critical factors for breast cancer intervention. Evaluation favors GoogleNet and ResNet-50, driving their selection for combined functionalities, ensuring improved precision, and dependability in microcalci fi cation detection in clinical settings. Methods: This study presents a comprehensive mammogram preprocessing framework using an optimized deep learning ensemble approach. The proposed framework begins with artifact removal using Otsu Segmentation and morphological operation. Subsequent steps include image resizing, adaptive median fi ltering, and deep convolutional neural network (D -CNN) development via transfer learning with ResNet-50 model. Hyperparameters are optimized, and ensemble optimization (AlexNet, GoogLeNet, VGG16, ResNet-50) are constructed to identify the localized area of microcalci fi cation. Rigorous evaluation protocol validates the ef fi cacy of individual models, culminating in the ensemble model demonstrating superior predictive accuracy. Results: Based on our analysis, the proposed ensemble model exhibited exceptional performance in the classi fi cation of microcalci fi cations. This was evidenced by the model ' s average con fi dence score, which indicated a high degree of dependability and certainty in differentiating these critical characteristics. The proposed model demonstrated a noteworthy average con fi dence level of 0.9305 in the classi fi cation of microcalci fi cation, outperforming alternative models and providing substantial insights into the dependability of the model. The average con fi dence of the ensemble model in classifying normal cases was 0.8859, which strengthened the model ' s consistent and dependable predictions. In addition, the ensemble models attained remarkably high performances in terms of accuracy, precision, recall, F1 -score, and area under the curve (AUC). Conclusion: The proposed model ' s thorough dataset integration and focus on average con fi dence ratings within classes improve clinical diagnosis accuracy and effectiveness for breast cancer. This study introduces a novel methodology that takes advantage of an ensemble model and rigorous evaluation standards to substantially improve the accuracy and dependability of breast cancer diagnostics, speci fi cally in the detection of microcalci fi cations.