Lung Cancer Detection Using CNN VGG19 + Model

Abstract

In the last many years, lung cancer has become a major public health concern. To examine cell breakdown in the lungs in its starting stages, doctors often use imaging modalities such as X-ray chest films, CT scans, MRIs, etc. The timing of diagnosis determines the course of therapy. Artificial intelligence (AI) is a hotspot for developing computational models of human intellect. In this research, we aim to enhance the detection and classification of lung nodules from CT images using a novel deep learning approach. Our study builds upon an extensive review of existing lung cancer detection methods, highlighting their strengths and weaknesses. This LIDC/IDRI dataset has been used in over half of the most recent research on lung cancer diagnosis. While several Convolutional Neural Networks (CNNs) architecture are adequate to process medical field data using technology solutions to discover and diagnose Lung cancer, here we proposed model VGG-19+ model. It is known for its strong feature extraction capabilities. It outflanks the ongoing model on a few exhibition measures, including exactness, accuracy, responsiveness/review, and f1-score.This research using VGG-19+ model could lead to more widespread utilization in cancer diagnosis by enhancing early lung cancer detection and developing the field of medical image analysis.