Citas bibligráficas
Casabona, P., Legonía, G. (2021). Desarrollo de un equipo portátil orientado a la automatización del diagnóstico rápido de dengue por flujo lateral, basado en algoritmos computacionales de procesamiento digital de imágenes [Tesis, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/657929
Casabona, P., Legonía, G. Desarrollo de un equipo portátil orientado a la automatización del diagnóstico rápido de dengue por flujo lateral, basado en algoritmos computacionales de procesamiento digital de imágenes [Tesis]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2021. http://hdl.handle.net/10757/657929
@misc{renati/397659,
title = "Desarrollo de un equipo portátil orientado a la automatización del diagnóstico rápido de dengue por flujo lateral, basado en algoritmos computacionales de procesamiento digital de imágenes",
author = "Legonía Carbajal, Gretta Lucero",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2021"
}
This dissertation proposes a portable electronic device that allows the rapid diagnosis of diseases using rapid immunochromatographic test or lateral flow assays (LFAs) which indicates whether the result is positive, negative, or invalid, these strips use blood serum as a sample. Nowadays there are similar solutions to which they have a very high cost, so the diagnosis is made by visual inspection at the Laboratorio de Biología Molecular del Instituto de Investigación de Enfermedades Infecciosas (IIEI); because of visual fatigue, the reliability of the results provided by laboratory technicians is reduced. In the scientific literature, the design of a device that requires a cell phone camera, laser lighting, the use of artificial vision algorithms and convolutional neural networks is proposed as a solution. This equipment uses computational algorithms for digital image processing on a photo taken by the integrated digital camera, such as trimming the area of interest, constructing histograms and acquisition of descriptors of the R, G and B components of the image taken by the camera, which will be used by a multilayer neural network. This software is complemented by hardware that includes a reduced board computer, which will issue the results through an interactive touch screen. According to what was obtained when using the algorithm, a degree of agreement greater than 0.9 was achieved, for this the Kappa Cohen index was used, mostly coinciding with the results issued by the laboratory technicians with those obtained by the proposed solution.
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