Lung cancer remains one of the leading causes of cancer-related deaths worldwide, alongside other
malignant diseases. Early and accurate detection of lung cancer is critical for improving patient survival
rates. This research presents a hybrid deep learning framework that combines Convolutional Neural
Networks (CNNs) and Recurrent Neural Networks (RNNs) to improve the accuracy of lung cancer
diagnosis from medical images. The proposed model outperforms traditional deep learning approaches
in terms of accuracy, precision, recall, and F1-score. Evaluation is conducted using publicly available
datasets relevant to lung cancer diagnosis. The study demonstrates the effectiveness of hybrid modeling
techniques in medical image analysis, paving the way for advanced AI-driven diagnostic systems in lung
cancer detection.
