| Summary: | Lung cancer remains the most lethal form of cancer, primarily due to late-stage diagnoses. Early detection significantly improves survival rates, yet remains challenging. This study aims to enhance early lung cancer diagnosis by developing and evaluating three models: a Multi-Layer Perceptron (MLP) for clinical data, a Convolutional Neural Network (CNN) for imaging data, and a hybrid model combining both data types. We hypothesized that integrating clinical and imaging data would yield higher diagnostic accuracy than single-modality approaches. Using the U.S. National Institute of Health’s (NIH) Prostate, Lung, Colorectal, and Ovarian (PLCO) dataset, which includes over 100,000 chest X-rays and associated clinical records, we preprocessed and balanced the data to train and test our models. The hybrid model achieved the highest accuracy (71.58%) compared to the MLP (70.88%) and CNN (58.25%) models, demonstrating the advantage of multi-modality integration. Despite facing class imbalance challenges, the study highlights the potential of combining clinical and imaging data for more accurate lung cancer diagnosis. Future research should consider adding other data sources like genetic and environmental factors to enhance the model's performance further. These findings underscore the promise of multi-modality approaches in transforming lung cancer diagnostics, potentially leading to earlier detection and improved patient outcomes.
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