Early detection of lung cancer significantly improves patient survival yet remains a challenge due to the subtle nature of early-stage radiological features. This study proposes a multimodal deep learning framework that combines convolutional neural networks (CNN) with dense neural networks (DNN) to enhance early-stage lung cancer prediction. A curated dataset comprising 2,000 patient records with CT scans and clinical metadata was preprocessed and used to train multiple models. The hybrid CNN-DNN model achieved the highest performance with an accuracy of 96.4%, precision of 95.8%, recall of 97.1%, F1-score of 96.4%, and AUC of 0.982, outperforming both traditional machine learning models and standalone CNNs. The integration of imaging and clinical features led to robust and accurate predictions, demonstrating strong potential for real-world clinical application. The results support the deployment of such AI-driven tools in diagnostic workflows to facilitate timely and accurate lung cancer detection.

Lung cancer detection, deep learning, convolutional neural networks (CNN), dense neural networks (DNN), early diagnosis, medical imaging, clinical integration, artificial intelligence (AI), hybrid model, computer-aided diagnosis

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