Integrating Spatial And Sequential Analysis For Lung Cancer Detection

Lung cancer is still one of the most common causes of death globally, and thus early diagnosis is critical to enhance survival. The conventional diagnostic procedures usually identify lung cancer at a late stage, making early detection important. This paper presents a deep learning-based model that combines MobileNet for efficient feature extraction and LSTM for analyzing sequential patterns to enhance early lung cancer detection. MobileNet effectively captures key medical image features, while LSTM identifies temporal dependencies, improving diagnostic accuracy. The model’s lightweight architecture allows for real-time deployment, making it particularly useful for mobile-based diagnostics and low-resource settings. With its high accuracy, this AI-powered solution holds great potential for clinical decision-making, early intervention, and cost-effective diagnosis.