Advanced Medical Image Processing for Lung Cancer Detection using Machine Learning Algorithms

One of the main causes of cancer-related deaths is lung cancer, and increasing survival rates requires early detection. The use of sophisticated machine learning (ML) algorithms to improve the identification of lung cancer from chest X-rays and CT images is investigated in this study. Vision Transformers (ViT), Reinforcement Learning (RL), Generative Adversarial Networks (GANs), Meta-Learning, and Ensemble Learning are some of the state-of-the-art methods we use. Compared to conventional CNNs, By capturing long-range dependencies in medical pictures, Vision Transformers (ViT) can improve accuracy by up to 85–96% compared to typical CNN models, especially when it comes to feature extraction and categorization. The optimization of diagnostic workflows using Reinforcement Learning (RL) results in a 90% improvement in decision-making efficiency and adaptive learning capabilities, which greatly enhance real-time picture analysis. Training datasets can be enhanced with Generative Adversarial Networks (GANs), which create realistic synthetic images and increase model generalization by 90–97%. This is crucial in situations where data is scarce or unbalanced. For uncommon or underrepresented cancer cases, meta-learning improves classification accuracy by 90% by allowing models to learn from sparsely labeled data. Ensemble learning reduces bias and variation by combining many models using approaches like XGBoost, Bagging, and Stacking, increasing total accuracy by 80–95%. When compared to conventional methods, key performance indicators including precision, recall, and F1 score demonstrate significant gains, with sensitivity rising by up to 90% and specificity improving by 85%. These cutting-edge algorithms greatly improve the detection of lung cancer, facilitating quicker and more precise diagnoses and assisting clinicians in making decisions that will benefit patients.