Multi-Class Classification of Lung Cancer Detection Using Deep Learning Model

Lung cancer has grown into an awful disease in recent years that places people's lives and good health at risk. Researchers and doctors have to use effective predictive modeling. In order to find some patterns in the data, this study provides a Naive Bayes-based lung cancer forecasting approach that looks at the association among a variety of readily available markers (like adenocarcinoma, large cell carcinoma, squamous cell carcinoma, normal, etc.) and lung cancer. Using a medical database and the technique can find a simple and intelligible lung cancer model, greatly lowering the chance of illness. The CNN classifiers are a set of basic probabilistic classifiers that use strong independence assumptions to apply the CNN theorem to an extensive variety of characteristics. It demonstrates that the CNN algorithm may significantly mitigate the issue, and it can accurately forecast how the frequency of these indications will alter the probability of developing lung cancer.