ADVANCED AI-DRIVEN FRAMEWORK FOR LUNG CANCER DETECTION USING CT SCAN IMAGES AND MOBILE APPLICATION INTEGRATION

Abstract

Lung cancer is among the leading causes of cancer-related deaths globally, primarily due to late-stage detection. Recent advancements in Artificial Intelligence (AI) and deep learning have opened new pathways for early and accurate diagnosis of lung cancer, addressing the limitations of traditional diagnostic methods. This study introduces an enhanced approach for processing lung CT (Computed Tomography) scan images using deep convolutional neural networks (CNNs) integrated with advanced noise reduction and feature extraction techniques. Unlike prior studies, our model dynamically applies adaptive thresholding using k-means clustering and morphological operations to isolate the lung region effectively and identify cancerous nodules. Additionally, we integrate 3D image meshing for improved visualization and analysis. To bridge the gap between diagnosis and patient awareness, the proposed model is supported by a mobile application ecosystem, enabling real-time health monitoring and communication. This research further emphasizes the potential of AI-based methodologies in detecting lung cancer at earlier stages, thereby reducing mortality rates. The paper also highlights directions for future advancements, such as staging cancerous tissues and improving diagnostic precision through more robust training datasets.