Hybrid Bio-Inspired and Artificial IntelligenceFrameworkfor Small Cell and Non-Small Cell LungCancerDetectionand Classification

Lung cancer is a serious disease and causes many deaths around the world. Finding it early is very important because it helps in better treatment and improving patient survival rates. Doctors usually use methods like CTscans, biopsies, and manual checking, but these methods can be slow and may not always be accurate. They also dependalot onthe doctor's experience and may miss cancer in the early stage. Artificial Intelligence (AI) helps improve diagnosisbyautomatically analyzing complex medical data. Deep Learning models can extract patterns from imaging and genomicdata, thereby improving detection and classification performance. Bio-inspired algorithms, which are based on natural processes, identify the most relevant features and help enhance the model's performance. Bio-inspired algorithms include GeneticAlgorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), and swarmbehaviorstooptimize feature selection. This research work presents a hybrid approach that combines AI-based models along withbioinspired optimization techniques for lung cancer detection and classification. When AI and bio-inspired algorithms areusedtogether, they yield better, faster results. This paper explains how these methods work together to detectionandclassification of lung cancer. The results show that combining these methods improves accuracy and helps doctors makebetter decisions.