Deep Learning-Based Prediction Of Lung Cancer Using Ct Scan Images: A Comparative Analysis Of Using Cnn Based

Abstract

Lung cancer is one of the deadliest diseases worldwide, making its early and precise detection vital for improving patient survival outcomes. This research presents a comparative evaluation of twenty-five CNN-based deep learning architectures for lung cancer classification using CT scan images. To ensure a fair and detailed analysis, multiple performance metrics such as Precision, Recall, F1-score, MSE, MAE, RMSE, Confusion Matrix, and AUC-ROC were employed. Each model was assessed not only on its classification accuracy but also on its ability to correctly identify class labels and minimize misclassifications. Among 25 models, SE-DenseNet201 emerged as the most effective, achieving the highest accuracy of 99.72% and outperforming all other architectures. The results highlight the strong potential of CNN-based models, with SE-DenseNet201 in particular showing excellent promise for supporting accurate lung cancer prediction and classification.