CVBN: Early Diagnosis of Breast Cancer Detection using Transfer Learning

One of the most common cancers afflicting women worldwide, breast cancer is characterized by uncontrolled cell growth in the breast tissue. Early detection and diagnosis are crucial for effective treatment and increased patient survival rates. Treatment results and patient survival rates are greatly improved by detecting breast cancer early. The optimal architecture for cancer prediction was suggested by this study as cascaded VGG19 with Bidirectional Long Short-Term Memory (CVBN). The VGG19 model is used to extract high-level characteristics from mammography pictures; it has been pre-trained on large image datasets. The Bi-LSTM network takes these characteristics and processes them, improving the model's prediction accuracy by capturing complex patterns and temporal correlations. The CVBN design outperforms conventional approaches on a large breast cancer dataset, proving its higher accuracy, sensitivity, and specificity. By enhancing feature extraction and segmentation, this integrated strategy successfully tackles the complexity and unpredictability of mammographic pictures, providing a potential tool for accurate and early identification of breast cancer.