ABSTRACT
This study improved lung cancer early diagnosis and therapy by utilising machine learning techniques, such as a customised Convolutional Neural Network (CNN), the Support Vector Machine (SVM), and deep learning models like VGG-19 and VGG-16. Models were trained after a variety of CT and MRI scan datasets were collected and preprocessed. Features were then retrieved. The VGG-19 tool had a remarkable accuracy rate of 97.65% in identifying the existence and stage of lung cancer, suggesting the tool's potential use in the diagnostic process. These results constitute a significant advancement in medical diagnostics and could lead to better patient outcomes as well as essential support for medical professionals in the prompt detection and management of this potentially fatal condition. The work demonstrates how machine learning may transform medical imaging and indicates that the future of sophisticated diagnostic tools for healthcare is bright.
