Optimized Residual Attention U-Net-Based Lung Cancer Detection

Lung cancer detection using CT pictures is critical for early detection. Deep learning methods can learn hierarchical representations of image characteristics, but detecting subtle patterns and anomalies is difficult. This study proposed an optimized residual attention U-Net for lung tumor detection using a Gaussian filter and the Binary Grasshopper Optimisation Algorithm (BGOA). The U-Net enhances feature extraction using multi-attention-gated modules and residual layers, broadening the receptive field while avoiding overfitting. The suggested Optimised Residual Attention U-Net increases classification accuracy by 96.66%, the F1-score by 87.98%, precision by 83.53%, AUC by 98.65%, specificity by 97.22%, and recall by 92.94%. This technique overcomes the issues of detecting subtle patterns and anomalies in CT images and improves the outcomes of lung cancer identification.