ENRNN-AU-Net: A Hybrid Deep Learning Model to Classify and Segment Histopathology Images of Breast Cancer

Breast cancer is a complicated and diverse ailment that requires thorough understanding at the cellular level to provide more accurate diagnoses and customized treatments. This work explores the categorization and division of breast cancer cells using imaging technology, computational algorithms, and histopathology examination. The classification result was derived by building a hybrid model that combined RNN and EfficientNetV2S with an accuracy of 99.99%, the model demonstrated promising improvement over the baseline, even though it acknowledged the influence of sparse data on outcomes. On the other hand, the segmentation result was derived by modifying the pre-trained model “U-Net” with an attention mechanism where the model was also able to achieve a significant accuracy of 99.54%.To achieve an understanding of the many subtleties of breast cancer, this study emphasizes the significance of classifying and segmenting cells at the individual cell level. It is possible to achieve a thorough understanding by using deep learning and machine learning models on image data.