Explainable artificial intelligence for detecting lung cancer

Early and reliable diagnosis of lung cancer is a major medical objective. This study makes a groundbreaking contribution to the field of smart healthcare by employing the capabilities of Explainable Artificial Intelligence (AI) and the Grad-CAM (Gradient-weighted Class Activation Mapping) visualization technique to improve lung cancer detection. The LIDC-IDRI dataset is used in the study to create a deep-learning model that can distinguish between benign and malignant lung diseases based on image features. This study demonstrates the importance of the Grad-CAM technique by highlighting the parts of medical images that have the most impact on the diagnostic choices made by the model. This method is in line with the developing norms of smart healthcare, where trust and transparency are of the utmost importance because it prioritizes classification accuracy and interpretability. The convincing findings of the study show that the model is highly accurate at distinguishing between benign and malignant instances. The model's diagnostic insights are equally impressive, but giving vivid and context-rich explanations really sets it apart. The model's usefulness in the actual world is boosted by incorporating the LIDC-IDRI dataset, which guarantees the diversity and authenticity of the data. This study provides a benchmark for progress in the field of smart healthcare since it balances cutting-edge AI capability with explainability. The results of this study could enhance patient outcomes by lowering mortality rates through earlier diagnosis and streamlining clinical processes. To fight lung cancer, AI-driven precision and interpretability offer a viable path through healthcare's complexity.