Early Detection of Lung Cancer using DenseNet with AI Support

There are challenges that arise if not detecting lung cancer at an early stage leads to the wastage of huge spending amounts of treatments, limited options at the final stage, a recovery rate would be less, and not achieving an expected accuracy rate than existing traditional methods. The combination of AI assistance at diagnostic time along with specific multi-modal diagnostic mechanisms and effective processing is initiated using DenseNet whose power is to reuse features, efficient feature learning, and efficient computational accuracy. The specific benefits ensured due to using AI and Densenet are reducing False positives, and False negatives due to its high sensitivity and specificity, results are consistent rather than human misinterpretation and misclassification experienced without AI is observed by validation. The results derived for this hybrid model in stages like AI at the diagnostic stage for analysis, and decision making, then processing using DenseNet ensures high accuracy when compared against the considered models.