Lung Cancer Prediction Using CNN Models

This paper discusses lung cancer prediction using different Convolutional Neural Networks (CNN). Lung cancer has the highest mortality rate with zero recovery. Deep learning (DL) uses various deep neural networks that work as human brains. CNN is used to forecast lung cancer; these algorithms of DL can be helpful in the recovery of patients by predicting the symptoms at the early stages. CNN works as a human brain with different hidden layers to solve the problem. Lung cancer is a social as well as economic cause for people who are unable to treat this disease due to lack of convenience or money. Medical area of research is having advancement with DL and its different algorithms. In this paper chest X-ray will take two directories named normal and pneumonia with three different stages, test, train, and validation (val) with two similar directories named as Normal and pneumonia. This paper uses Sequential and DenseNet models for the prediction of lung cancer. Densenet works better than sequential models with an accuracy of 95.86% respectively.