Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

Breast cancer is a significant threat to the global population, affecting not only women but also a threat to the entire population. With recent advancements in digital pathology, Eosin and hematoxylin images provide enhanced clarity in examining microscopic features of breast tissues based on their staining properties. Early cancer detection facilitates the quickening of the therapeutic process, thereby increasing survival rates. The analysis made by medical professionals, especially pathologists, is time-consuming and challenging, and there arises a need for automated breast cancer detection systems. The upcoming artificial intelligence platforms, especially deep learning models, play an important role in image diagnosis and prediction. Initially, the histopathology biopsy images are taken from standard data sources. Further, the gathered images are given as input to the Multi-Scale Dilated Vision Transformer, where the essential features are acquired. Subsequently, the features are subjected to the Bidirectional Long Short-Term Memory (Bi-LSTM) for classifying the breast cancer disorder. The efficacy of the model is evaluated using divergent metrics. When compared with other methods, the proposed work reveals that it offers impressive results for detection.