Advanced Deep Learning Framework for Automated Lung Cancer Detection Using DeepResNet

This study presents a sophisticated framework for automatically identifying lung cancer nodules from CT scans using a unique DeepResNet model. Our approach combines the segmentation abilities of UNet with the feature extraction capabilities of ResNet, resulting in accurate identification and classification of pulmonary nodules. The model's performance is assessed using the LIDC-IDRI dataset, showcasing exceptional metrics: an accuracy of $\mathbf{9 9 . 8 9 \%}$, an F1 score of $\mathbf{9 9 . 8 3 2 \%}$, a sensitivity of $100 \%$, a precision of $99.832 \%$, and a specificity of $\mathbf{9 9 . 7 8 8 3} \%$. This blend deep learning architecture improves both the accuracy of diagnoses and the ability to detect subtle characteristics of nodules that are important for diagnosing early-stage lung cancer.