Lung Cancer Detection with Deep Neural Network

Abstract

By using state of the art deep learning models on the Iraq Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) dataset, a novel advancement in lung cancer prediction is demonstrated in this study. Our analysis shows the Compact Convolutional Transformers (CCT) to be the clear choice among five cutting edge models, with an incredible accuracy of 99.09%. Building on this achievement, we carried out an in depth ablation study to further optimize the CCT. The effects of optimizers, learning rates, loss functions, batch sizes, and pooling techniques were examined in detail in this study. A careful adjustment of these parameters produced a notable improvement in accuracy, highlighting the crucial part that fine tuning performs in building predictive models. Further, we conducted a thorough investigation using significant metrics such confusion matrices, classification reports, Area Under the Curve (AUC) scores, and loss curves to verify the robustness of our method. The model performed quite well, classifying cases properly and providing detailed insights into its recall and precision. The most significant conclusion of our research is that our best model reaches an astounding accuracy of 99.09%, highlighting its potential as an effective tool for early lung cancer identification. This achievement highlights the value of using deep learning in medical diagnostics in addition to marking a significant improvement in predicted accuracy.