Applications and Advancements of Nanoparticle-Based Drug Delivery in Lung Cancer Treatment

In the recent decade, cancer nanomedicine has revolutionized cancer detection, diagnosis, and treatment via nanotechnology. The National Institutes of Health defines nanoparticle as any substance that reduces a medicine's size to 1 micron. Nanoparticle-based therapy systems' ability to carry hydrophobic medications, specifically target ill areas, and overcome biological barriers has garnered attention. The framework provided in this proposed includes data preparation, feature selection, and model training. When ready for usage, data is cleaned, normalized, and missing data filled. To optimize model learning, feature selection is done before training. Fuzzy RBF-CNNs integrate convolutional neural networks, fuzzy clustering, and radial basis function neural networks to train the model. With an average accuracy of 94.26%, the Fuzzy RBF-CNN model outperformed CNN and SVM models. The framework's feature selection and learning efficiency improvements improve prediction results. Compared to CNN and SVM, Fuzzy RBF-CNN performs better. The hybrid nature of the model makes cancer nanomedicine applications strong and accurate, which could improve treatment efficiency and precision.