Revolutionizing Lassa fever prevention: Cutting-edge MATLAB image processing for non-invasive disease control

Lassa fever, a viral haemorrhagic illness endemic to West Africa, poses significant public health challenges. Conventional diagnostic methods are often invasive and time-consuming, leading to delayed intervention. This study explores the integration of MATLAB-based image processing as a modern, non-invasive approach to Lassa fever prevention and control. Leveraging advanced machine learning algorithms within MATLAB, this framework aims to detect early symptoms, assess infection risk, and monitor disease progression. The proposed system enhances diagnostic accuracy, reduces the need for invasive procedures, and provides timely intervention. This article details the theoretical foundations, methodologies, and practical implications of using MATLAB for image processing in Lassa fever management. Future directions are discussed, emphasizing the potential for scalable, low-cost solutions that could revolutionize public health responses to viral epidemics.