Hybrid Artificial Neural Networks for Lung Cancer Detection

Lung cancer remains a major global health concern with elevated attributable mortality., primarily attributed to the asymptomatic nature of the disease, manifesting itself at the advanced, untreatable stage. The life span of a patient with lung cancer if it is diagnosed early and appropriately. AI plays a role of improving the early diagnosis of lung cancer. To predict lung cancer data, we have created and implemented a Hybrid Feed Forward Artificial Neural Network (HFF-ANN) optimized by different algorithm. Various performance measures were used to assess our model; among the optimizers used, the Adam optimizer provided the highest accuracy to our model making it achieve a perfect 98.4% accuracy level. The results prove that the development of AI in lung cancer diagnosis as early-stage detection and treatment options is feasible, explainable., and effective, using neural networks with algorithm enhancements. Some of those affected by the disease are likely to survive if it occurs as some medical research presented will suggest.