Lung cancer classification using convolutional neural network and DenseNet

Abstract

Lung cancer is a condition that has a major impact on public health. Convolutional Neural Network (CNN) and DenseNet approaches are suggested in this study to aid lung cancer detection and classification. In various fields of pattern recognition and medical imaging, CNN and DenseNet have demonstrated their efficacy. In this study, radiology images from individuals with lung cancer were used to create a set of medical lung images. The findings show that lung cancer can be accurately classified into malignant and benign from radiological images using CNN and DenseNet architectures, with a parameter accuracy of 99.48%. This research contributes to the creation of a deep learning-based system for detecting and classifying lung cancer. The findings can be the basis for creating a more accurate and productive lung cancer diagnostic system.