Physics-informed hybrid models for enhanced precision in breast cancer classification

Breast cancer poses a significant health and safety risk for women globally, making early detection vital for effective treatment. Artificial intelligence (AI) can enhance early detection by differentiating between cancerous and non-cancerous breast tissues. Existing AI approaches for breast cancer detection face limitations, including overfitting, the need for fine-tuning, and loss of fine details. This research introduces an adaptive hybrid model infused with physics insights framework to address these challenges. The transformed dynamic and adaptive filter is proposed for data preprocessing and achieving a balance between noise reduction and edge preservation, thereby retaining critical image structures. Then, robotic physics informed model is proposed which contains diffusion convolution, batch normalization layers, activation layers, pooling layers, optimization layer and ends with a classification layer. The proposed approach compared with the baseline AOADL-HBCC, DTLRO-HCBC, Inception v3, Inception v3 Long Short Term Memory, Inception v3 Bi-directional Long Short Term Memory, VGG-16, and Residual Network such as 96.77%, 93.52%, 81.67%, 91.46%, 92.05%, 80.15%, and 82.18%, respectively. The accuracy of the proposed approach is 99.56%. This demonstrates our model’s superior performance and effectiveness in breast cancer detection.