Enhanced Transformers with Dilated Convolutional Attention for Predicting Lung Cancer Outcomes

The study in investigation of the usage of dilated convolutional attention functions with transformers to improve the accuracy of the prognosis of lung cancer. The suggested model exploits the benefits of the dilated convolutions to seize multi-scale spatial features in medical related data while it is integrating the human attention of the transformers that are focused on the most important information. The predicted model needs to cover collected clinical data patterns from which the best results for lung cancer will be achieved. These patterns are including early detection and tracking of tumor progression. this alternative the most effective way to improve feature extraction, but it also ensures presenting the most important trends in medical data to achieve an exact prognosis. The proposed methodology is an structured methodology that health experts use to diagnose tumor at variable stages of patient's testing while also giving hints of the possibility of Lyme Infections.