A Deep Learning Approach For Personalized Precision Medicine For Cancer Treatment

Current research combines multiple machine learning models to provide comprehensive insights into tailor-made medicine for lung cancer. The study identified micropatterns of malignant disorders using a dataset of 3222 CT and MRI scans using VGG16 architecture, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN). A comprehensive understanding of the characteristics of pulmonary arteries is obtained. After extensive evaluation, the models exhibit exceptional accuracy and precision, with VGG16 performing particularly well. Confusion matrices provide comprehensive insight into model predictions. This work provides important new information in the field of personalized precision medicine through the potential of standardized methods for lung cancer diagnosis and treatment. Future research should focus on sample design they will be enhanced, and their application will be extended to multiple datasets. © 2024 IEEE.