Optimized AI-Based Detection of Pulmonary Nodules Using VGG16 and XGBoost

A novel approach for earlier lung cancer detection is combining the XGBoost classifier with a Histogram Focused on Gradients (HOG) data and VGG16, a deep learning model. With a startling accuracy range of 97–98%, our ensemble-based approach clearly beats conventional techniques. Celebrated for its ability to catch minute details in images, the VGG16 model stresses structural components and performs quite elegantly with HOG properties. Feeding these data into the XGBoost classifier—known for speed and performance—results in a quite accurate and dependable lung cancer detection tool. Our results clearly show the benefits over traditional approaches; thus, they emphasize the likely usage of this new approach in clinical environments. Given its high accuracy, our model seems to be a useful instrument for early lung cancer detection; this is essential to raising patient survival rates and outcomes. Future larger datasets will allow us to test our model more thoroughly and investigate its integration into clinical procedures. Moreover, evaluating the model on other imaging modalities and different patient groups will help to guarantee its general relevance and strength. In the end, this research provides the means for the construction of sophisticated diagnostic instruments able to change early lung cancer diagnosis and, therefore, improve the general results of treatment