(untitled) (International Journal of Latest Technology in Engineering Management & Applied Science, 2026)
The rapid growth of air travel and global logis- tics has intensified the need for efficient and reliable security screening systems. Conventional baggage inspection relies heavily on human interpretation of Computed Tomography (CT) and X-ray images, which is time-consuming and prone to fatigue- related errors. This paper presents VEGA, an AI-based software framework that enhances luggage threat detection by intelligently analyzing the output of existing CT and X-ray scanners. Instead of focusing on scanning hardware or radiation physics, VEGA applies deep learning techniques to pre-captured scan images to identify and classify objects within luggage. The system performs multi-dimensional image analysis, highlights suspicious regions, and assigns threat confidence scores to assist security operators. Experimental results demonstrate improved detection accuracy and reduced false alarms compared to manual screening. VEGA offers a scalable and cost-effective solution for integrating AI into modern airport, border, and logistics security workflows.