Paper Contents
Abstract
In this work, we present a VGG-16 based deeplearning algorithm for early lung cancer neuropathology.Given that over 142,670 people died from lung cancer in theUS in 2019 alone, earlier detection is crucial to improvingsurvival results. Typical diagnostic methods aretime-consuming and liable to error. With the help ofpreprocessing X-ray images and training a CNN model(combination with VGG-16), our proposed solution will be ableto automate for identification. Flatten model normalizing,Dense and dropout layer for efficiency in identification of lungcancer. This is a fresh paradigm of approach to fight lungcancer for making early detection possible and better patientoutcomes.
Copyright
Copyright © 2024 Likhitha S. This is an open access article distributed under the Creative Commons Attribution License.
