Enhancing lung cancer detection with hybrid CNN models

Lung cancer is still one of the deadliest diseases in the world, which proves the importance of effective and early diagnosis. This paper proposes AI-Lung Detect, a new system that aims to improve lung cancer detection using a combination of Convolutional Neural Network (CNN). In its essence, the system employs ResNet-50, a highly advanced deep learning model, to identify high-level features from CT scans that are characteristic of cancerous tissues. These features are then classified using optimized ensemble classifiers to ensure that the imaging data is classified accurately as normal or cancerous findings. The framework is trained and validated on a large dataset from Kaggle to make it more reliable and accurate. The findings show that the proposed approach of using ResNet-50 with optimized ensemble classifiers is more accurate, precise, and recall-oriented than the conventional methods. Further work will be directed towards the creation of a MATLAB application with a graphical user interface to enhance the clinical application of AI-Lung Detect. This initiative is to offer a strong and effective means for medical practitioners in the fight against this deadly disease.