1 Automation and Process control Engineer, Gist Limited United Kingdom.
2 Department of Electrical Engineering and Computer Science EECS Washkewicz College of Engineering Cleveland State University USA.
3 Registered Nurse, RM Nigeria.
4 Senior Program Officer, WH Gates Institute for Population and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(02), 1179–1202
Received on 05 July 2024; revised on 12 August 2024; accepted on 14 August 2024
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.
Lassa Fever; MATLAB; Image Processing; Machine Learning; Non-Invasive Diagnosis; Public Health; Viral Haemorrhagic Fever; Disease Control
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Joseph Chukwunweike, Habeeb Dolapo Salaudeen, Adewale Mubaraq and Victor Imuwahen Igharo. Revolutionizing Lassa fever prevention: Cutting-edge MATLAB image processing for non-invasive disease control. World Journal of Advanced Research and Reviews, 2024, 23(2), 1179-1202. Article DOI: https://doi.org/10.30574/wjarr.2024.23.2.2471