Enhancing Breast Cancer Diagnosis: A Deep Belief Network Approach for Mammography Image Analysis

Breast cancer remains a pressing global health challenge, emphasizing the need for reliable diagnostic methods. This research explores the imperative of crafting dependable diagnostic models for breast cancer through mammogram images, bridging an existing knowledge gap. We introduce an innovative Deep Learning model centered on Deep Belief Networks (DBNs) for Breast Cancer Diagnosis. Our primary objective is to evaluate the algorithm's AUC, sensitivity, and specificity metrics. By fine-tuning weights and biases in a layered DBN structure and leveraging the esteemed Digital Database for Screening Mammography (DDSM) dataset, we aim to elevate classification accuracy. The DBN model exhibits potential in differentiating between benign and malignant breast conditions. Remarkably, the model boasts an accuracy rate of 89%, a sensitivity of 85%, and an AUC value of 0.91, among other notable metrics. These findings underscore the DBN algorithm's capability to advance breast cancer detection, potentially facilitating prompt diagnoses and enhancing patient prognosis. Nonetheless, for its effective deployment in clinical scenarios, rigorous validation, thorough testing, and juxtaposition against top-tier methods remain crucial.