Colorectal Cancer Detection based on Integrated Graph Convolutional Networks and Shift Window Transformer

Advanced predictive analytics is required to improve early identification and patient outcomes for colorectal cancer (CRC), which is still a major worldwide health concern. Using deep learning and graph-based methods, this study suggests a new prediction framework that makes use of Graph Convolutional Networks (GCNs) and a Shifted Window Transformer architecture by means of thorough pre-processing, which includes graph creation and normalization, we establish a strong input structure for our model. To improve feature extraction from histopathology pictures, the Shifted Window Transformer-which is well-known for its effectiveness in capturing long-range dependencies-is incorporated. The model is able to utilize both individual cellular properties and their interrelations thanks to GCNs that learn from the graph structure. By automatically identifying colorectal cancer in its early stages, this prediction system seeks to do away with the requirement for human identification by medical professionals and allow for prompt treatment without awaiting a formal diagnosis. This work aims to improve early-stage (stage 1) detection using our suggested automated models because it is challenging to detect CRC in stage 0. This innovative strategy promises prompt interventions that will enhance patient care and clinical results.